Natural Products at the Crossroads of Chemical Biology and Systematics: A New Paradigm for Drug Discovery

Abstract

This review synthesizes contemporary advances and methodologies at the intersection of natural product research, chemical biology, and biological systematics. It explores the foundational relationship between taxonomic classification and metabolic diversity, highlighting how chemosystematic approaches guide the discovery of novel bioactive compounds. The article critically assesses cutting-edge technologies—including cell-free biosynthetic systems, AI-powered target prediction, and integrated multi-omics strategies—for elucidating and exploiting natural product function. It further addresses persistent challenges in characterization and supply, offering optimization frameworks for troubleshooting isolation and production bottlenecks. By validating these approaches through comparative analysis of chemical space and clinical success stories, this work provides a comprehensive resource for researchers and drug development professionals aiming to harness natural products for therapeutic innovation.

The Chemosystematic Link: How Taxonomy Guides Chemical Discovery

Natural products (NPs) represent a cornerstone of chemical biology and therapeutic development, offering unparalleled structural diversity honed by millions of years of evolutionary selection [1]. These compounds, originating from plants, fungi, bacteria, and marine organisms, function as defense chemicals, signaling agents, and ecological mediators, making them particularly valuable for drug discovery and systematics research. Their chemical space is characterized by elevated molecular complexity, including higher proportions of sp³-hybridized carbon atoms, increased oxygenation, and rigid molecular frameworks that facilitate optimal interactions with biological targets [1]. Within this expansive chemical universe, four major classes—terpenoids, alkaloids, polyketides, and peptides—emerge as fundamental pillars, each with distinct biosynthetic origins, structural features, and biological activities. The study of these compounds now integrates traditional methodologies with modern technological platforms including genome mining, synthetic biology, and artificial intelligence, creating a powerful framework for elucidating biosynthetic pathways and engineering novel bioactive molecules [2] [1].

Terpenoids: Structural Diversity and Biosynthetic Engineering

Structural Classification and Biosynthetic Pathways

Terpenoids, also known as isoprenoids, constitute one of the largest and most structurally diverse families of natural products, with over 80,000 identified compounds [2]. These metabolites are biosynthesized through two primary pathways: the mevalonate (MVA) pathway in the cytosol of eukaryotes and some bacteria, and the methyl-D-erythritol-4-phosphate (MEP) pathway in prokaryotes and plant plastids. The fundamental building blocks, isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), are condensed by prenyltransferases (PTs) to generate prenyl diphosphates of varying chain lengths (C₅, C₁₀, C₁₅, C₂₀, C₂₅, C₃₀). Terpene synthases (TSs) then catalyze the cyclization and rearrangement of these linear precursors into diverse carbon skeletons, which are further functionalized by tailoring enzymes such as cytochrome P450s oxygenases (P450), glycosyltransferases (GT), and acyltransferases (ACT) [2].

Table 1: Major Terpenoid Subclasses and Representative Structures

Subclass	Carbon Skeleton	Representative Compounds	Biological Activities
Monoterpenoids	C₁₀	Menthol, Limonene	Antimicrobial, flavoring agents
Sesquiterpenoids	C₁₅	Artemisinin [2], Bisabolene	Antimalarial [2], anti-inflammatory [2]
Diterpenoids	Câ‚‚â‚€	Paclitaxel [2], Retinol	Anticancer [2], vitamin A precursor
Triterpenoids	C₃₀	Squalene, Lanosterol	Sterol precursors, anti-inflammatory
Tetraterpenoids	C₄₀	β-Carotene, Lycopene	Antioxidants, vitamin A precursors

Experimental Approaches for Terpenoid Discovery and Production

The exploration of terpenoid chemical space has been revolutionized by integrated approaches that combine genomics, synthetic biology, and analytical technologies.

Genome Mining and Heterologous Expression: Identification of terpene synthase genes (TSs) through genome sequencing enables the discovery of novel terpenoid pathways. Functional characterization often requires heterologous expression in microbial hosts such as Saccharomyces cerevisiae or Aspergillus oryzae [2]. The Heterologous EXpression (HEX) synthetic biology platform has enabled high-throughput screening of numerous fungal terpene and polyketide gene clusters in S. cerevisiae, leading to the identification of previously inaccessible terpenoids [2].

Automated High-Throughput Workflows: Automated workstations facilitate the transfer of numerous terpene gene clusters into yeast, with subsequent cultivation in microtiter plates. Metabolite extraction and LC-MS/MS analysis enable rapid structural characterization of terpene products, significantly accelerating discovery timelines [2].

Metabolic Engineering for High-Yield Production: The "Targeted Synthetic Metabolism" strategy involves optimizing protein ratios in terpene biosynthetic pathways through in vitro titration reactions, followed by systematic engineering of these pathways in microbial hosts to achieve stable and efficient synthesis of high-value terpenes [2]. This approach addresses the challenge of low product yields that often impedes bioactivity evaluation.

Figure 1: High-Throughput Terpenoid Discovery Workflow

Alkaloids: Nitrogen-Containing Bioactive Compounds

Structural Classification and Biosynthetic Origins

Alkaloids are low-molecular-weight nitrogenous compounds, typically basic in nature, that contain one or more nitrogen atoms, usually within a heterocyclic ring [3] [4]. These compounds are biosynthesized primarily from amino acid precursors such as tyrosine, phenylalanine, tryptophan, lysine, or ornithine, though some incorporate terpenoid or other structural moieties [5]. The structural diversity of alkaloids arises from variations in their carbon skeletons, nitrogen incorporation patterns, and post-modification reactions including oxidation, methylation, and glycosylation.

Table 2: Major Alkaloid Classes and Their Characteristics

Class	Amino Acid Precursor	Representative Compounds	Pharmacological Activities
Pyrrolidine & Tropane	Ornithine	Cocaine [3], Hyoscyamine	Stimulant, anticholinergic [3]
Piperidine & Quinolizidine	Lysine	Coniine [3], Lupinine	Toxic, nicotinic activity [3]
Indole	Tryptophan	Vinblastine, Strychnine [3]	Anticancer, toxic [3]
Isoquinoline	Tyrosine	Morphine [3], Codeine	Analgesic [3]
Imidazole	Histidine	Pilocarpine	Parasympathomimetic
Terpenoid	Secologanin/Tryptophan	Dendrobine [5]	Neuroprotective, anti-viral [5]

The genus Dendrobium exemplifies alkaloid diversity, with at least 60 structurally characterized alkaloids including 35 sesquiterpene alkaloids, 14 indolizidine alkaloids, five pyrrolidine alkaloids, four phthalide alkaloids, two organic amine alkaloids, one imidazole type, and one indole alkaloid [5]. Dendrobine from D. nobile has demonstrated significant neuroprotective effects in cortical neurons injured by oxygen-glucose deprivation/reperfusion and prevents Aβ₂₅₋₃₅-induced neuronal and synaptic loss [5].

Experimental Protocols for Alkaloid Research

Biosynthetic Pathway Elucidation: Alkaloid biosynthesis involves complex, often compartmentalized pathways that can span multiple cell types. In Catharanthus roseus, for example, the biosynthesis of vinblastine and vincristine involves different enzymatic steps in various cellular compartments, with final assembly steps occurring in a different cell type than the early steps, necessitating intercellular transport of metabolic intermediates [4]. Modern approaches combine stable isotope labeling (¹³C, ¹⁵N, ²H) with NMR and MS analysis to trace precursor incorporation [6]. Gene knockout experiments in producing organisms help identify biosynthetic intermediates and shunt products.

Heterologous Production in Microbial Hosts: Reconstruction of alkaloid biosynthetic pathways in microorganisms like E. coli and S. cerevisiae enables production of complex alkaloids and novel analogs. For benzylisoquinoline alkaloid biosynthesis, researchers have expressed plant-derived norcoclaurine synthase (NCS), 6-O-methyltransferase (6OMT), coclaurine N-methyltransferase (CNMT), and 4′-O-methyltransferase (4′OMT) along with a microbial monoamine oxidase (MAO) to synthesize reticuline from dopamine in E. coli [4]. Further expression of tailoring enzymes in S. cerevisiae has enabled production of magnoflorine and scoulerine from reticuline [4].

Analytical Techniques for Alkaloid Characterization:

• Extraction: Plant material is typically extracted with methanol or ethanol under reflux, followed by acid-base extraction to separate alkaloids from other compounds.
• Separation: Crude extracts are fractionated using silica gel column chromatography, with increasing polarity of organic solvents.
• Purification: Final purification employs techniques including preparative TLC, HPLC, or countercurrent chromatography.
• Structural Elucidation: Advanced NMR (¹H, ¹³C, 2D experiments) and high-resolution mass spectrometry provide structural information.

Figure 2: Generalized Alkaloid Biosynthesis Pathway

Polyketides: Modular Assembly Line Biosynthesis

Structural Diversity and Biosynthetic Logic

Polyketides represent one of the largest classes of natural products with significant medicinal applications, including antibiotic, antifungal, anticancer, and immunosuppressant activities [7]. These compounds are synthesized by polyketide synthases (PKSs), which share a core biosynthetic logic with fatty acid synthases, iteratively building complex molecules from simple precursors like acetyl-CoA and malonyl-CoA [7]. The structural diversity of polyketides arises from variations in the selection of extender units, the degree of β-carbon processing after each condensation, and post-assembly tailoring reactions.

Type II polyketide synthases are iterative enzymes that produce aromatic compounds through a minimal PKS consisting of a ketosynthase chain-length factor (KS-CLF) heterodimer and an acyl carrier protein (ACP) [7]. The nascent poly-β-ketone chain undergoes specific cyclization and aromatization patterns dictated by the KS-CLF, followed by tailoring modifications such as oxidations, glycosylations, and methylations.

Engineering Polyketide Biosynthesis

Gene Knock-Out and Mutational Analysis: Systematic gene knock-out experiments in producing organisms enable elucidation of biosynthetic pathways and isolation of intermediates. In the mupirocin biosynthetic pathway (mup gene cluster), mutation of specific genes resulted in a complete switch from production of primarily pseudomonic acid A (PA-A) to exclusive production of PA-B, revealing unexpected biosynthetic relationships [6]. Similar experiments with the thiomarinol BGC (tml) produced marinolic acid and related analogs lacking the pyrrothine moiety [6].

Combinatorial Biosynthesis and Pathway Engineering: Domain swapping between related PKS systems generates hybrid enzymes that produce novel polyketides. In fungal tenellin and bassianin biosynthesis, which involve multi-domain PKS-NRPS hybrids, domain swapping between the two biosynthetic gene clusters followed by heterologous expression in Aspergillus oryzae produced numerous new metabolites in high yields, revealing key elements controlling polyketide chain length and methylation patterns [6].

Optimization of Production Strains: Genetic engineering of producing strains can improve titers and simplify metabolite profiles. For instance, engineering of Pseudomonas fluorescens to block the 10,11-epoxidation in mupirocin biosynthesis diverted the pathway to produce exclusively pseudomonic acid C (PA-C) as the main product, which demonstrated improved stability while retaining antibiotic activity [6].

Table 3: Experimentally Determined Production Yields of Engineered Polyketides

Polyketide	Native Producer	Engineered System	Yield Improvement	Key Modification
Pseudomonic Acid C	Pseudomonas fluorescens	Engineered P. fluorescens (Δoxidase)	High titre as sole product [6]	Blocked 10,11-epoxidation [6]
Novel Tenellin Analogs	Beauvaria species	Aspergillus oryzae (heterologous)	High yields [6]	Domain swapping between PKS-NRPS [6]
Marinolic Acid	Pseudoalteromonas sp.	Pseudoalteromonas sp. (ΔNRPS)	Main product [6]	Deletion of NRPS gene cluster [6]

Peptides: Structural and Functional Diversity

Ribosomal and Non-Ribosomal Peptides

Bioactive peptides from natural sources represent a rapidly expanding class of therapeutics with diverse applications, including antimicrobial, antioxidant, antihypertensive, and anticancer activities [8]. These compounds are broadly categorized into ribosomal peptides (synthesized through the translation machinery and often post-translationally modified) and non-ribosomal peptides (synthesized by NRPS enzymes without direct RNA template).

Apidaecin, a proline-rich antimicrobial peptide (PrAMP) produced by honeybees (Apis mellifera), represents a novel class of non-lytic antimicrobials that inhibit bacterial growth by targeting intracellular processes rather than disrupting membranes [9]. Apidaecin Ib (H-GNNRPVYIPQPRPPHPRL-OH) and its synthetic derivative Api-137 exhibit activity against Gram-negative bacteria including E. coli, P. aeruginosa, and K. pneumonia by inhibiting translation termination through stabilization of the quaternary complex of ribosome-apidaecin-tRNA-release factor [9].

Structure-Activity Relationship Studies

Structural Modifications and Functional Analysis: Structure-activity relationship (SAR) studies of apidaecin have identified the C-terminal five amino acids (P/z-H/z-P-R-X, where z = aromatic amino acid and X = any amino acid except A,S,G) as the core pharmacophore responsible for antimicrobial activity [9]. Modifications of key residues dramatically affect potency:

• Replacement of Arg17 with homoarginine (sidechain length difference) reduced activity 16-fold (MIC = 2.5 μM vs. 0.16 μM for Api-137)
• Replacement with citrulline (urea instead of guanidinium) reduced activity 128-fold (MIC = 20 μM) due to altered H-bonding capacity [9]
• Mono-methylation of Arg17 retained near-native activity (MIC = 0.3 μM) [9]

C-Terminal Modifications: The carboxylic acid of the C-terminal Leu18 is essential for activity, as replacement with decarboxy-leucine (complete removal of carboxylic acid) abolished antimicrobial activity (MIC > 40 μM) [9]. Substitution with leucinol or phenylalaninol (carboxyl replaced with alcohol) reduced but did not eliminate activity, with MIC values of 5 μM for l-leucinol and l-phenylalaninol derivatives [9].

Antioxidant Peptide SAR: Antioxidant peptides from natural proteins exhibit structure-activity relationships dependent on amino acid composition, sequence, and molecular weight. Key features enhancing antioxidant activity include:

• Presence of hydrophobic amino acids (Leu, Val, Phe, Trp) that improve lipid solubility and interaction with free radical species
• Histidine-containing peptides that exhibit metal ion chelating capacity and radical scavenging
• Low molecular weight peptides (typically 500-1500 Da) with enhanced cellular absorption
• Specific amino acid sequences that determine hydrogen-donating ability and electron transfer efficiency [8]

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Research Reagents for Natural Product Investigation

Reagent/Category	Specific Examples	Function/Application
Heterologous Host Systems	Saccharomyces cerevisiae, Aspergillus oryzae, E. coli	Expression of biosynthetic gene clusters from diverse organisms [2] [6]
Gene Editing Tools	CRISPR-Cas systems, Recombineering protocols	Targeted gene knock-outs, pathway engineering, activation of silent clusters [1]
Bioinformatics Platforms	antiSMASH [6], DeepBGC, GNPS, Pfam [2]	Genome mining, BGC identification, metabolite annotation [2] [1]
Analytical Standards	Stable isotope-labeled precursors (¹³C, ¹⁵N, ²H)	Metabolic flux analysis, biosynthetic pathway elucidation [6]
Enzyme Assay Components	SAM (S-adenosyl methionine), NADPH, acetyl-CoA	In vitro characterization of tailoring enzymes, substrate specificity studies
Chromatography Materials	Silica gel, C18 reverse-phase, Sephadex LH-20	Extraction, fractionation, and purification of natural products [3]
3,5-Dichloro-2-hydroxybenzamide	3,5-Dichloro-2-hydroxybenzamide|CAS 17892-26-1	3,5-Dichloro-2-hydroxybenzamide is a chemical intermediate for research. This product is For Research Use Only (RUO). Not for human or veterinary use.
Tris(2,2,6,6-tetramethylheptane-3,5-dionato-O,O')praseodymium	Tris(2,2,6,6-tetramethylheptane-3,5-dionato-O,O')praseodymium, CAS:15492-48-5, MF:C33H57O6Pr, MW:690.7 g/mol	Chemical Reagent

Integrated Discovery Workflows in Chemical Biology

Modern natural product research employs integrated workflows that combine multi-omics technologies, synthetic biology, and computational approaches to navigate chemical space efficiently.

Genome-Mining Guided Discovery: The standard workflow begins with genome sequencing of potential producer organisms, followed by bioinformatic analysis using tools like antiSMASH to identify biosynthetic gene clusters (BGCs) [2] [1]. Clusters of interest are prioritized based on novelty indices and phylogenetic analysis compared to known BGCs. Selected clusters are then activated through various strategies: heterologous expression in optimized chassis strains, promoter engineering in native hosts, or cultivation under simulated natural environmental conditions using iChip technology [1].

AI-Enhanced Natural Product Discovery: Artificial intelligence and machine learning algorithms are increasingly applied to predict BGC boundaries, substrate specificity of biosynthetic enzymes, and even three-dimensional structures of novel natural products [2] [1]. Tools like DeepBGC and related platforms use deep learning to identify BGCs in genomic data and predict their chemical products, enabling virtual screening of potentially valuable metabolites before undertaking laborious experimental work [1].

Sustainable Sourcing and Production: To address ecological concerns associated with traditional natural product sourcing, researchers are developing sustainable alternatives including optimized cultivation of producer organisms, microbial fermentation of plant-derived metabolites, and complete synthesis of complex natural products in engineered microbial hosts [1]. These approaches reduce pressure on natural ecosystems while ensuring consistent and scalable production of valuable compounds.

Figure 3: Integrated Natural Product Discovery Workflow

The systematic exploration of terpenoids, alkaloids, polyketides, and peptides reveals both the remarkable structural diversity of natural products and the underlying biosynthetic logic that generates this chemical space. Within chemical biology and systematics research, these compound classes provide invaluable insights into evolutionary biochemistry while serving as privileged scaffolds for therapeutic development. Contemporary research has transitioned from traditional discovery approaches to integrated platforms that combine genomics, synthetic biology, and computational methods, enabling both the identification of novel structures and the engineering of improved analogs. As these technologies continue to mature, particularly with advances in AI-guided prediction and sustainable bioproduction, natural products will remain essential to addressing emerging health challenges and advancing fundamental understanding of chemical biological systems.

The integration of metabolite-content similarity into taxonomic and systematic research represents a paradigm shift in how scientists classify organisms and understand evolutionary relationships. This approach leverages the fundamental principle that organisms produce characteristic sets of small molecules through evolutionary processes, creating chemical profiles that reflect phylogenetic relationships. While traditional taxonomy has relied heavily on morphological characteristics and, more recently, genomic data, metabolite profiling offers a complementary perspective that captures functional biochemical adaptations. This technical guide examines the theoretical foundations, methodological frameworks, and practical applications of metabolite-content similarity as a taxonomic marker across plant and microbial kingdoms, with particular relevance to natural products research in chemical biology and drug discovery. The evidence presented demonstrates that chemical classification not only corroborates established phylogenetic relationships but also provides unique insights into functional ecological adaptations and bioactive potential that may not be apparent from genetic data alone.

Metabolite-content similarity as a taxonomic approach operates on the core premise that secondary metabolites—bioactive substances with diverse chemical structures—have evolved in response to ecological selection pressures and thus reflect evolutionary relationships among organisms [10]. Higher plants inhabiting different ecological environments employ distinct combinations of secondary metabolites for adaptation, suggesting that similarity in metabolite content can effectively indicate phylogenetic similarity [10]. This chemical systematics approach has gained significant traction as analytical technologies have advanced to enable comprehensive metabolomic profiling.

The evolutionary rationale for metabolite-based classification stems from the observation that secondary metabolites are often conserved within taxonomic groups while exhibiting sufficient diversity to distinguish between them. These compounds, including alkaloids, flavonoids, terpenoids, and phenolics, serve ecological functions in defense against herbivores, pathogenic microbes, and environmental stressors [11]. Their structural diversity arises from evolutionary processes that make them excellent markers for tracing phylogenetic lineages and adaptations [12].

In the context of natural products research, metabolite-based taxonomy offers practical advantages for drug discovery by creating associations between taxonomic groups and specific bioactivities. As noted by [12], "Through the natural selection process, natural products possess a unique and vast chemical diversity and have been evolved for optimal interactions with biological macromolecules." This establishes a powerful link between chemical classification and bioprospecting efforts.

Methodological Frameworks for Metabolite-Based Classification

Analytical Techniques for Metabolite Profiling

The reliability of metabolite-content similarity as a taxonomic marker depends heavily on the analytical methods employed for metabolite detection and characterization. Multiple complementary techniques provide comprehensive chemical profiles for taxonomic comparisons.

Table 1: Analytical Techniques in Metabolite-Based Taxonomy

Technique	Application in Chemotaxonomy	Resolution	References
Liquid Chromatography-Mass Spectrometry (LC-MS)	Comprehensive profiling of secondary metabolites	High sensitivity for diverse chemical classes	[11]
Gas Chromatography-Mass Spectrometry (GC-MS)	Volatile compound analysis, primary metabolism	Excellent for volatile and semi-volatile compounds	[11]
Nuclear Magnetic Resonance (NMR) Spectroscopy	Structural elucidation, quantitative analysis	Non-destructive, provides structural information	[11]
UV Spectroscopy	Preliminary screening, compound class determination	Rapid but limited structural information	[11]
Fourier-Transform Infrared (FTIR) Spectroscopy	Functional group analysis, chemical fingerprinting	Rapid classification based on functional groups	[11]
MALDI-TOF MS	High-throughput profiling, imaging mass spectrometry	Spatial distribution of metabolites	[11]

Data Processing and Similarity Assessment

The transformation of raw analytical data into meaningful taxonomic information requires specialized computational approaches. A critical first step involves determining structural similarity between metabolites, typically using the Tanimoto coefficient (also known as Jaccard similarity coefficient) [10]. This measure calculates the proportion of molecular features shared by two compounds divided by their union, with values ranging from 0-1 (higher values indicating greater similarity):

[ \text{Tanimoto}_{A,B} = \frac{A \cap B}{A + B - A \cap B} ]

where (A) and (B) represent the molecular features of two metabolites [10]. Empirically, a Tanimoto coefficient value larger than 0.85 indicates highly similar bioactive compounds [10].

For organism classification, plants or microbes are represented as binary vectors indicating presence or absence relationships with structurally similar metabolite groups [10]. This approach compensates for incomplete metabolomics data by focusing on metabolite groups rather than individual compounds. Similarity between organisms is then calculated using binary similarity coefficients, which are transformed into distance measures for clustering analysis [10].

Classification Algorithms

Hierarchical clustering methods, particularly Ward's method, have been successfully applied to classify plants based on metabolite-content similarity, producing clusters consistent with known evolutionary relations [10]. Additional machine learning approaches such as Support Vector Machines (SVM) have been employed to classify plants by economic uses based on metabolite profiles, demonstrating the predictive power of metabolite content for exploring nutritional and medicinal properties [10].

Applications in Plant Taxonomy

Case Study: Classification of 216 Plant Species

A landmark study demonstrating the efficacy of metabolite-content similarity in plant taxonomy involved the successful classification of 216 plants based on known but incomplete metabolite content data [10]. The methodology employed in this research serves as a prototype for metabolite-based taxonomic approaches:

Experimental Protocol:

• Data Collection: Species-metabolite relationships were obtained from the KNApSAcK Core Database, containing 109,976 species-metabolite relationships encompassing 22,399 species and 50,897 metabolites [10]
• Molecular Structure Analysis: Structural description files (MOL files) for metabolites were acquired from KNApSAcK and PubChem databases, with atom pair fingerprints generated using the R package ChemmineR [10]
• Structural Similarity Network: Metabolites were clustered using the DPClus network clustering algorithm based on Tanimoto coefficient calculations [10]
• Plant Representation: Plants were represented as binary vectors indicating relations with structurally similar metabolite groups rather than individual metabolites [10]
• Classification: Hierarchical clustering using Ward's method was applied to similarity matrices derived from binary similarity coefficients [10]

Validation: The resulting plant clusters showed remarkable consistency with known evolutionary relations from NCBI taxonomy, despite the incomplete nature of the metabolomics data [10]. This demonstrates that metabolite content possesses significant taxonomic value as a complementary approach to molecular phylogenetic methods.

Chemotaxonomy of Medicinal Plants

Chemotaxonomy has proven particularly valuable in the identification and classification of medicinal plants, where precise authentication is critical for efficacy and safety [11]. The approach relies on the consistent presence of characteristic secondary metabolites within taxonomic groups:

Table 2: Key Secondary Metabolite Classes in Plant Chemotaxonomy

Metabolite Class	Chemical Characteristics	Taxonomic Utility	Medicinal Relevance
Alkaloids	Nitrogen-containing compounds, basic properties	Family-specific distribution (e.g., Papaveraceae)	Analgesic, antimicrobial activities
Flavonoids	Polyphenolic structures, 15-carbon skeleton	Species differentiation within genera	Antioxidant, anti-inflammatory
Terpenoids	Isoprene unit derivatives, diverse structures	Genus and species level discrimination	Anticancer, antimicrobial
Phenolic Compounds	Hydroxylated aromatic rings	Chemotype identification	Antioxidant, neuroprotective
Plant Peptides	Short amino acid chains	Recent application in taxonomy	Antimicrobial, signaling

The integration of chemotaxonomy with DNA barcoding and morphological assessment creates a powerful hybrid identification system that enhances accuracy, particularly for commercially processed plant materials where morphological features may be lost [11].

Applications in Microbial Taxonomy

Metabolic Pathway Similarity for Phylogenetic Reconstruction

Comparative analysis of metabolic pathways has emerged as a robust approach for microbial classification. [13] demonstrated that phylogenetic trees could be derived from similarity analysis of metabolic pathways based on enzyme-enzyme relational graphs. This technique defines distance measures between graphs using node similarity (enzymes) and structural relationships, applying these to metabolic pathways such as the Citric Acid Cycle and Glycolysis across different organisms [13].

The resulting phylogenetic trees showed remarkable concordance with established phylogenies while revealing previously unrecognized relationships among organisms [13]. This approach considers complete metabolic processes rather than individual components, potentially providing a more comprehensive view of functional evolution.

microbeMASST: A Taxonomically Informed Metabolomics Tool

The recent development of microbeMASST represents a significant advancement in microbial metabolite-based taxonomy [14]. This taxonomically informed mass spectrometry search tool addresses the critical challenge of limited microbial metabolite annotation in untargeted metabolomics experiments.

Key Features and Capabilities:

• Database of >60,000 microbial monocultures from diverse environments (plants, soils, oceans, humans) [14]
• Categorization according to NCBI taxonomy at multiple taxonomic levels [14]
• Ability to link both known and unknown MS/MS spectra to microbial producers via fragmentation patterns [14]
• Fast Search Tool implementation enabling rapid database queries [14]

Experimental Workflow:

• Microbial cultures are established from target environments
• LC-MS/MS analysis generates metabolic profiles
• MS/MS spectra are searched against the microbeMASST database
• Matching algorithms identify microbial producers of specific metabolites
• Results are visualized in interactive taxonomic trees with statistical support

Validation Studies: microbeMASST successfully connected known microbial metabolites to their producers, including lovastatin exclusively to Aspergillus species and salinosporamide A specifically to Salinispora tropica [14]. The tool also revealed unexpected connections, such as the widespread production of commendamide across multiple bacterial genera [14].

Integration with Systems Biology and Metabolic Modeling

The application of systems biology approaches represents the cutting edge of metabolite-based taxonomy, particularly through the development of Genome-Scale Metabolic Models (GEMs) [15]. These computational models enable the prediction of metabolic capabilities directly from genomic information, creating a bridge between genetic potential and chemical expression.

Metabolic Modeling of Plant-Microbe Interactions

Plant-microbe interactions are fundamentally mediated by metabolites, creating complex exchange networks that systems biology aims to decipher [15]. Flux Balance Analysis (FBA) approaches applied to GEMs can predict metabolic interactions between organisms, including host-microbiome relationships [15]. Recent advancements, such as Expression and Thermodynamics Flux (ETFL) models, incorporate protein synthesis constraints and thermodynamic principles to improve prediction accuracy [15].

Challenges in Metabolic Modeling for Taxonomy

Despite promising developments, several challenges remain in fully leveraging metabolic modeling for taxonomic purposes:

• Parameterization difficulty: Substrate uptake rates and kinetic parameters are rarely available for all metabolites [15]
• Macromolecule integration: The focus on small molecules often neglects the taxonomic importance of macromolecular interactions [15]
• Multi-scale complexity: Integrating metabolic models across cellular, tissue, and organism levels presents computational challenges [15]
• Environmental variability: Metabolic responses to different growth conditions complicate standardized classification [15]

Table 3: Essential Research Resources for Metabolite-Based Taxonomy

Resource/Reagent	Application	Key Features	References
KNApSAcK Database	Plant-metabolite relationship data	109,976 species-metabolite relationships, 50,897 metabolites	[10]
microbeMASST	Microbial metabolite annotation	60,781 LC-MS/MS files, 541 microbial strains, NCBI taxonomy mapping	[14]
PubChem Database	Metabolite structure information	SDF files for structural similarity calculations	[10]
ChemmineR (R package)	Chemical similarity analysis	Tanimoto coefficient calculation, structural clustering	[10]
DPClus Algorithm	Network clustering	Identification of structurally similar metabolite groups	[10]
GNPS Ecosystem	Mass spectrometry data analysis	Spectral matching, molecular networking	[14]
AntiSMASH	Biosynthetic gene cluster detection	Prediction of secondary metabolite pathways	[1]

Metabolite-content similarity has established itself as a robust taxonomic marker that complements traditional morphological and molecular approaches. The evidence from both plant and microbial kingdoms demonstrates that chemical profiles reflect evolutionary relationships while providing unique insights into functional adaptations and ecological niches. The methodological frameworks outlined in this technical guide provide researchers with standardized approaches for implementing metabolite-based classification in diverse taxonomic contexts.

Future developments in this field will likely focus on several key areas:

• Integration of multi-omics data: Combining metabolomic, genomic, and transcriptomic data for comprehensive taxonomic assessment [11] [15]
• Artificial intelligence applications: Machine learning and deep learning algorithms for pattern recognition in complex metabolomic datasets [11] [1]
• Expanded reference databases: Continued growth of curated metabolite databases with improved taxonomic annotations [10] [14]
• Standardization efforts: Development of standardized protocols for metabolite-based taxonomy to enable cross-study comparisons [11]
• Field-deployable technologies: Miniaturized mass spectrometry and spectroscopic tools for real-time chemical classification in ecological studies

As these advancements mature, metabolite-content similarity will play an increasingly important role in systematic research, natural product discovery, and understanding evolutionary relationships across the tree of life.

The search for novel bioactive natural products is a cornerstone of pharmaceutical research, particularly in the development of new antibiotics. A central paradigm guiding this discovery process is the observed correlation between taxonomic distance and the diversity of secondary metabolites produced by microorganisms. This paradigm posits that examining phylogenetically distant taxa, such as new genera or families, significantly increases the likelihood of discovering novel chemical scaffolds compared to further sampling within well-studied genera. This whitepaper examines the robust evidence supporting this taxonomy paradigm, details the experimental methodologies that validate it, and discusses its critical implications for future natural product discovery and microbial systematics.

Quantitative Evidence for the Taxonomy Paradigm

Key Evidence from Systematic Metabolite Surveys

A large-scale systematic metabolite survey of the bacterial order Myxococcales provides compelling quantitative evidence for the taxonomy paradigm. The study, which analyzed approximately 2,300 bacterial strains using liquid chromatography-mass spectrometry (LC-MS), found a clear correlation between taxonomic distance and the production of distinct secondary metabolite families [16].

Table 1: Distribution of Known Metabolites in Myxococcales

Taxonomic Level	Finding	Implication for Discovery
Genus Level	Existence of unique or highly genus-specific compound families [16]	Chances of discovering novel metabolites are greater by examining strains from new genera.
Sub-genus Level	Clustering based on known metabolites allocated most data sets into genus-featuring clades [16]	Significant inter-genera variations exist in the secondary metabolome.
Species Level	General tendency toward species-typical compounds, though less distinct than genus-level separation [16]	Species-level discovery is feasible but may offer diminishing returns compared to genus-level exploration.

The analysis revealed that a striking subset of compound families was either unique to a single genus or demonstrated high genus specificity. This pattern provides strong evidence for the existence of distinct chemotypes corresponding to taxonomic divisions [16]. The findings further support the strategy of prioritizing the exploration of new genera to increase the probability of finding novel natural product scaffolds, a approach that has already led to the discovery of new structures like rowithocin, which features an uncommon phosphorylated polyketide scaffold [16].

Supporting Evidence from Other Taxonomic Groups

The correlation between taxonomy and secondary metabolite production is not confined to myxobacteria. Studies on the fungal genus Aspergillus have similarly demonstrated that secondary metabolite profiles are highly species-specific and can be used effectively in species recognition and classification [17].

Table 2: Supporting Evidence from Diverse Taxonomic Groups

Organism Group	Evidence for Taxonomy-Chemistry Correlation	Reference
Aspergillus Section Nigri	Specific secondary metabolite profiles characterise each species; "chemoconsistency" is pronounced.	[17]
Pomegranate (Punica granatum L.)	Significant variation in secondary metabolites (flavonoids, tannins) among different accessions, influenced by environmental factors.	[18]
Lactobacillaceae Family	Genome mining reveals a richness of biosynthetic gene clusters (BGCs), with most having unknown functions, indicating vast unexplored chemical diversity.	[19]

In Aspergillus, the classification based on morphological, physiological, and chemical features shows excellent agreement with phylogenetic groupings based on β-tubulin sequencing, illustrating a strong link between evolutionary history and chemical capacity [17]. This "chemophylogeny" provides a powerful framework for targeting taxonomic groups with high probabilities of yielding novel chemistries.

Experimental Methodologies for Validating the Paradigm

Standardized Workflow for Metabolite Correlation Studies

Establishing a robust correlation between taxonomy and metabolite production requires a standardized, high-throughput workflow from strain selection to data analysis. The following diagram illustrates the integrated experimental protocol based on the myxobacteria study [16].

Diagram 1: Experimental workflow for metabolite-taxonomy correlation.

Detailed Experimental Protocols

Strain Selection and Cultivation

• Strain Collection: Select a diverse set of microbial strains that achieve high coverage of the target taxonomy. For myxobacteria, this involved ~2,300 strains representing a wide phylogenetic breadth within the order [16].
• Cultivation: Grow all strains in empirically optimized, genus-typical cultivation media to ensure expression of secondary metabolite pathways. Standardize protocols for temperature, aeration, and incubation time to minimize technical variation [16].

Metabolite Extraction and Analysis

• Extraction: Process bacterial cultures according to standardized protocols for metabolite extraction, using consistent solvent systems, volumes, and drying procedures [16].
• LC-MS Analysis: Analyze all extracts using uniform Liquid Chromatography-Mass Spectrometry (LC-MS) conditions. High-resolution mass spectrometry is critical for accurate mass determination and initial structural characterization [16].

Data Processing and Analysis

• Metabolite Annotation: Process LC-MS data sets to examine for known compounds based on accurate mass-to-charge ratio (m/z), retention time, and isotope pattern fits to reference data from purified compounds [16].
• Creation of Distribution Matrix: Construct a compound distribution matrix comparing compound family occurrence against taxonomic classification (suborder, family, genus) [16].
• Statistical Clustering: Perform hierarchical clustering of individual, blinded data sets based on their metabolite production profiles to determine if they self-organize into taxonomically relevant clades without prior classification input [16].

Genomic Foundations of the Taxonomy Paradigm

Genome Mining and Biosynthetic Gene Cluster Diversity

The taxonomy paradigm is strongly supported by genomic evidence, as the genetic potential for secondary metabolite production is encoded within Biosynthetic Gene Clusters (BGCs). Comprehensive analysis of bacterial genomes reveals that phylogenetically distinct organisms harbor unique BGCs, suggesting they can produce novel compounds [20].

Advanced computational platforms like PRISM 4 enable the prediction of chemical structures from genomic sequences, facilitating the targeted discovery of novel antibiotics. PRISM 4 uses 1,772 hidden Markov models (HMMs) and implements 618 in silico tailoring reactions to predict the structures of 16 different classes of secondary metabolites [20]. When applied to 3,759 bacterial genomes, PRISM 4 predicted thousands of encoded antibiotics, with a particular abundance of novel BGCs in phylogenetically distinct bacterial phyla such as Desulfobacterota, Spirochaetota, and Campylobacterota [20]. This genomic-based approach corroborates the metabolite survey findings and provides a powerful tool for prioritizing microbial taxa for experimental investigation.

From Genotype to Chemotype: Bridging the Gap

A significant challenge in natural product research is the gap between the genomic capacity of a strain (genotype) and the metabolites observed under laboratory cultivation conditions (chemotype) [16]. This disconnect underscores the importance of complementing genomic predictions with empirical metabolite profiling, as exemplified by the integrated workflow in Section 3.

The correlation between taxonomic distance and BGC diversity suggests that exploring new genera provides access not only to new BGC sequences but also to novel enzymatic transformations and biosynthetic logic, which are the foundations of chemical diversity [20].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Research Reagents and Solutions for Metabolite-Taxonomy Studies

Reagent/Solution	Function in Research	Technical Specification
Optimized Cultivation Media	To support the growth of diverse microbial taxa and elicit the production of secondary metabolites.	Empirically developed for specific taxonomic groups; composition varies by genus [16].
LC-MS Grade Solvents	For high-performance liquid chromatography-mass spectrometry analysis of metabolite extracts.	High purity (e.g., ≥99.9%) to minimize background noise and ion suppression [16].
Metabolite Standard Libraries	For dereplication of known compounds via comparison of m/z, retention time, and isotope patterns.	Curated in-house databases containing characterized metabolites from the studied taxa [16].
Genomic DNA Extraction Kits	To obtain high-quality DNA for sequencing and BGC analysis.	Must be suitable for the specific microbial group (e.g., Gram-negative bacteria, fungi).
PCR Reagents for BGC Amplification	To amplify and sequence specific biosynthetic gene clusters of interest.	Include high-fidelity DNA polymerases and cluster-specific primers [20].
Bioinformatics Software (e.g., PRISM 4)	To predict chemical structures of secondary metabolites from genomic sequences.	Utilizes HMMs and reaction rules for in silico pathway reconstruction [20].
N,N'-Bis(8-aminooctyl)-1,8-octanediamine	N,N'-Bis(8-aminooctyl)-1,8-octanediamine, CAS:15518-46-4, MF:C24H54N4, MW:398.7 g/mol	Chemical Reagent
Leucomycin A4	Leucomycin A4, CAS:18361-46-1, MF:C41H67NO15, MW:814.0 g/mol	Chemical Reagent

The taxonomy paradigm—that a strong correlation exists between taxonomic distance and secondary metabolite diversity—is robustly supported by both large-scale empirical metabolomic studies and comprehensive genomic analyses. This paradigm provides a powerful strategic framework for maximizing the efficiency and success of natural product discovery campaigns. By prioritizing the exploration of phylogenetically novel and underexplored microbial genera, researchers can significantly increase their chances of discovering unprecedented chemical scaffolds with potential bioactivities. As genomic and metabolomic technologies continue to advance, their integrated application within this taxonomic framework will undoubtedly continue to reveal the vast, untapped chemical potential of the microbial world, fueling the next generation of therapeutic agents.

In the fields of chemical biology and systematics, the evolutionary history of organisms, or phylogeny, provides a powerful framework for understanding and predicting the structural diversity of natural products. Natural products, also known as secondary metabolites, are chemical compounds produced by organisms such as bacteria, fungi, and plants that often possess potent biological activities. These molecules have historically been an essential source for drug discovery, with natural products and their derivatives accounting for a significant proportion of newly approved drugs, including first-in-class therapeutics [21] [12]. The structural classes of these compounds—including polyketides, nonribosomal peptides, terpenoids, and alkaloids—are not randomly distributed across the tree of life but are instead linked to the evolutionary histories of their producing organisms [22].

The core thesis of this whitepaper is that phylogenetic relationships are highly informative for delineating the architecture and function of genes involved in secondary metabolite biosynthesis. By applying molecular phylogenetics to the study of biogenic pathways, researchers can create predictive models that connect taxonomic identity with chemical structural classes. This approach, often termed phylogenomics, has been enabled by the vast increase in publicly available genomic sequence data and sophisticated bioinformatic tools [22]. This guide provides an in-depth technical overview of the methodologies, applications, and experimental protocols for mapping structural classes to biological sources, offering researchers and drug development professionals a comprehensive resource for leveraging phylogeny in natural product discovery.

Fundamental Concepts: Phylogenetics and Biosynthetic Logic

Principles of Molecular Phylogenetics

Phylogenetics is the study of evolutionary relatedness among groups of organisms based on molecular sequence data. The results of these analyses are typically represented as phylogenetic trees—diagrams whose branches represent evolutionary lineages and whose nodes represent inferred speciation events or gene duplication events [23] [24]. Two fundamental concepts in molecular phylogenetics are critical for natural product research:

• Orthologs vs. Paralogs: Orthologs are homologs produced by speciation (divergence of species), while paralogs are produced by gene duplication within a genome. Using orthologs is essential for inferring species relationships, as the use of paralogs can lead to incorrect phylogenetic inferences due to incomplete sampling of duplicated gene families [23].
• Rooted vs. Unrooted Trees: A rooted tree has a designated node representing the most common ancestor of all entities in the tree, providing directionality to evolutionary relationships. An unrooted tree shows relatedness without assumptions about ancestry, with the most probable root often placed at the midpoint of the longest path between taxa [23].

Molecular phylogenies are inferred using various optimality criteria, including maximum parsimony (favoring the tree requiring the fewest evolutionary changes), maximum likelihood (seeking the tree with the highest probability given the sequence data and an evolutionary model), and Bayesian inference (which incorporates prior knowledge to estimate the posterior probability of trees) [24].

Biosynthetic Logic of Major Natural Product Classes

The structural diversity of natural products arises from specific, evolutionarily conserved biosynthetic logic. Two of the most extensively studied enzyme systems are polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs), which are responsible for assembling many clinically valuable microbial metabolites [22].

• Polyketide Synthases (PKS): Polyketides are polymers of acetate and other simple carboxylic acids that display remarkable structural diversity due to their combinatorial assembly process. PKSs are multienzyme complexes that sequentially construct polyketides in an assembly-line fashion. They are broadly categorized into three types (I-III), with type I PKSs being particularly diverse and often modular in architecture [22].
• Nonribosomal Peptide Synthetases (NRPS): NRPSs assemble structurally complex peptides from amino acid building blocks without direct RNA template guidance. Similar to PKSs, they function as modular assembly lines, with each module typically consisting of condensation (C), adenylation (A), and thiolation (T) domains responsible for activating, incorporating, and carrying the growing peptide chain [22].

Table 1: Major Natural Product Biosynthetic Systems and Their Characteristics

Biosynthetic System	Building Blocks	Key Enzymes/Domains	Representative Products
Polyketide Synthases (PKS)	Carboxylic acids (e.g., acetate, malonate)	Ketosynthase (KS), Acyltransferase (AT), Ketoreductase (KR)	Erythromycin, Tetracycline [22]
Nonribosomal Peptide Synthetases (NRPS)	Amino acids	Condensation (C), Adenylation (A), Thiolation (T)	Cyclosporine, Penicillin [22]
Hybrid PKS-NRPS	Carboxylic acids & Amino acids	KS, AT, C, A, T	Rapamycin, Epothilone [12]

The evolutionary history of these biosynthetic systems is complex, involving processes such as gene duplication, recombination, and horizontal gene transfer (HGT), which collectively generate new structural diversity [22].

Methodologies: Integrating Phylogenetics and Pathway Analysis

Phylogenetic Analysis of Biosynthetic Gene Clusters

The genes encoding natural product biosynthetic pathways are typically organized in clusters in microbial genomes. Phylogenetic analysis of specific domains within these clusters can reveal relationships that predict structural features of the final metabolic product.

• Ketosynthase (KS) Domain Phylogeny: KS domains are the most conserved domains in PKSs and form highly predictive clades in phylogenetic trees. Different KS clades correspond to distinct enzyme architectures or biochemical functions. For example, a specific KS clade comprises iterative type I PKSs that produce enediynes, a class of potent anticancer agents including calicheamicin. Finer phylogenetic resolution within this clade can even distinguish between genes producing 9- or 10-membered core enediyne ring structures [22].
• Adenylation (A) Domain Phylogeny in NRPS: A domains are responsible for selecting and activating specific amino acid building blocks in NRPS assembly lines. The phylogeny of A domains can be used to predict the substrate specificity of each module, thereby informing predictions about the amino acid sequence of the resulting nonribosomal peptide [22].

The following diagram illustrates the generalized workflow for conducting a phylogenetic analysis of a biosynthetic gene cluster to map structural classes to biological sources.

Metabolic Pathway Alignment and Functional Module Mapping

Beyond single genes or domains, entire metabolic pathways can be compared across organisms to infer phylogenetic relationships. A method called MMAL (Multiple Metabolic Pathway Alignment) transforms the alignment of multiple pathways into constructing a union graph, identifies functional modules within this graph, and builds mappings between these modules [25]. The similarity between pathways is then computed by comparing the mapped functional modules, and phylogenetic relationships are inferred from these similarities.

Experimental results demonstrate that this approach can correctly categorize organisms into main groups with specific metabolic characteristics. For instance, analysis of 16 organisms showed that the two archaea included in the study (Archaeoglobus fulgidus and Methanocaldococcus jannaschii) consistently formed a distinct group, suggesting that archaea have particular metabolic pathway characteristics different from other species [25]. This methodology reveals that pathway topologies are the result of a compromise between phylogenetic information inherited from a common ancestor and evolutionary pressures that cause more rapid shifts in metabolic structure [26].

Taxon Sampling and Robustness Considerations

Judicious taxon sampling is critical in phylogenetic analysis, as poor sampling may result in incorrect inferences. Theoretical causes for inaccuracy include long branch attraction, where non-related branches are incorrectly grouped by shared nucleotide sites [24]. Research has shown that, when working with a fixed number of total nucleotide sites, sampling fewer taxa with more sites (genes) per taxon often yields higher bootstrapping replicability and accuracy than sampling more taxa with fewer sites per taxon [24]. However, increasing the number of genes compared per taxon can be challenging for uncommonly sampled organisms due to unbalanced genomic databases.

Table 2: Key Bioinformatics Tools for Phylogenetic Analysis of Natural Product Biosynthesis

Tool Name	Primary Function	Application in Natural Product Research	Reference
NaPDoS (Natural Product Domain Seeker)	Classifies KS and C domains using phylogenetic logic	Predicts enzyme architecture and biochemical function from sequence data [22]
antiSMASH	Identifies secondary metabolite biosynthetic gene clusters	In silico analysis of gene cluster architecture and potential products [22]
IsoRankN	Global multiple-network alignment tool	Used for phylogenetic reconstruction from multiple metabolic pathways [25]
PHYLIP	Software package for inferring phylogenetic trees	Builds phylogenetic trees from distance matrices (e.g., using neighbor-joining) [25]

Experimental Protocols

Protocol: Building a Reliable Phylogenetic Tree for Biosynthetic Genes

This protocol outlines the key steps for constructing a phylogenetic tree from PKS KS or NRPS C domains to predict structural features of the resulting natural products [22].

• Sequence Acquisition and Curation:

  • Retrieve amino acid sequences of target biosynthetic domains (e.g., KS domains for PKS, C domains for NRPS) from databases or sequencing projects.
  • Manually inspect sequences for completeness and the presence of key catalytic residues. Exclude fragments or sequences with obvious errors.

• Multiple Sequence Alignment:

  • Use alignment software such as MUSCLE or ClustalW to create a multiple sequence alignment.
  • Visually inspect the alignment and manually refine if necessary to ensure proper alignment of conserved motifs.

• Phylogenetic Tree Inference:

  • Select an evolutionary model that best fits the alignment data using model-testing software (e.g., ProtTest for protein sequences).
  • Construct an initial tree using a fast method such as Neighbor-Joining.
  • Perform rigorous phylogenetic analysis using Maximum Likelihood (e.g., with RAxML) or Bayesian inference (e.g., with MrBayes).
  • Execute multiple independent runs to assess convergence.

• Tree Assessment and Interpretation:

  • Evaluate branch support using bootstrap analysis (for Maximum Likelihood) or posterior probabilities (for Bayesian inference).
  • Map known functional and structural data from characterized gene clusters onto the tree to identify clades associated with specific architectural features or product structural classes.

Protocol: Phylogenetic Analysis of Entire Metabolic Pathways

This protocol describes a method for inferring phylogenetic relationships by aligning multiple metabolic pathways based on topological similarities, as exemplified by the MMAL framework [25].

• Data Retrieval and Pathway Definition:

  • Retrieve metabolic pathway data for a set of target organisms from the KEGG database.
  • Define the set of common metabolic pathways to be analyzed across all organisms.

• Union Graph Construction and Module Mapping:

  • Transform the alignment of multiple metabolic pathways into the construction of a union graph of these pathways.
  • Cluster the nodes in the union graph to identify functional modules within the pathways.
  • Build mappings between the functional modules of different organisms' pathways.

• Distance Matrix Calculation and Tree Building:

  • Compute the similarity between pathways by comparing the mapped functional modules.
  • Produce a distance matrix for the set of organisms based on the pathway topology similarities.
  • Build a phylogenetic tree from the distance matrix using a neighbor-joining algorithm as implemented in software such as PHYLIP.

• Tree Validation and Comparison:

  • Compare the resulting pathway-based phylogenetic tree to a reference tree (e.g., based on 16S rRNA) using software such as COUSINS to assess similarity based on cousin pairs.
  • Analyze the tree to identify clusters that reflect both phylogenetic relationships and specific metabolic characteristics.

Table 3: Essential Research Reagents and Computational Tools for Phylogenetically-Guided Natural Product Research

Reagent/Tool	Function/Purpose	Example/Notes
KEGG Database	Reference database for metabolic pathways and genes	Used to retrieve organism-specific pathway data for comparative analysis [25]
16S rRNA Sequence Data	Molecular marker for constructing reference organismal phylogenies	Serves as a benchmark for evaluating metabolic pathway-based trees [25]
PHYLIP Software Package	Infers phylogenetic trees from sequence or distance data	Used with neighbor-joining algorithm to build trees from pathway-based distance matrices [25]
NaPDoS Web Tool	Phylogenetically classifies KS and C domains from sequence data	Predicts biosynthetic function and links sequences to structural classes [22]
antiSMASH	Identifies and annotates biosynthetic gene clusters in genomic data	Provides first-pass in silico analysis of natural product potential [22]
Global Natural Products Social Molecular Networking (GNPS)	Community resource for sharing and curating mass spectrometry data	Aids in metabolite identification and cross-referencing structural data [21]

The integration of phylogenetics with the study of biogenic pathways represents a powerful paradigm shift in natural product research. By applying evolutionary thinking to the genes, domains, and pathways responsible for secondary metabolite biosynthesis, researchers can create predictive models that efficiently link biological sources to structural classes. This approach adds a layer of insight to traditional phyletic reconstruction from a metabolic standpoint and offers a rational strategy for prioritizing organisms and gene clusters for drug discovery efforts [25] [22].

Future advancements in this field will be driven by the increasing availability of genomic data from diverse taxa, improvements in algorithms for phylogenetic inference and pathway comparison, and the development of more sophisticated bioinformatic tools that integrate phylogenetic prediction with structural elucidation. As these methodologies mature, phylogenetically guided discovery will continue to enhance our understanding of natural product evolution and accelerate the identification of novel bioactive compounds with therapeutic potential.

Chemosystematics, also referred to as chemotaxonomy, represents a critical interdisciplinary field that utilizes the chemical constituents of organisms to elucidate taxonomic relationships and evolutionary pathways [27] [28]. Originally an unwritten knowledge system for distinguishing useful from harmful plants, it has evolved into a formalized science that integrates chemistry, phylogenetics, and natural product research [27]. This whitepaper delineates the theoretical foundations of chemosystematics, highlighting how advancements in analytical technologies, particularly metabolomics, have solidified the correlation between an organism's chemical profile and its evolutionary history [29] [30]. The core thesis is that secondary metabolites, produced through evolutionarily conserved biosynthetic pathways, provide a robust chemical record that complements morphological and molecular data, thereby offering invaluable insights for systematic biology and modern drug discovery [12] [30].

The fundamental principle of chemosystematics is that the presence, absence, or proportional distribution of specific chemical compounds within organisms can reveal phylogenetic relationships and evolutionary divergence [27] [28]. These chemical profiles, especially those of secondary metabolites, are the phenotypic expression of deep-seated genetic and enzymatic processes that are subject to natural selection [29]. Consequently, the metabolic architecture of a plant is more closely linked to its genotype than many classic morphological traits [29]. Historically, this knowledge was applied informally; however, the field has been progressively formalized, with useful, harmful, and inactive chemical constituents from relevant taxa now identified and recorded [27].

The close relationship between chemical profiles and evolutionary relationships is evidenced by the high degree of concordance between established taxonomy and chemotaxonomy at the genus level [30]. This positions chemosystematics as a powerful bridge between evolution and chemistry, providing a chemical window into the evolutionary history of life.

The Evolutionary and Biochemical Basis

Secondary Metabolites as Evolutionary Endpoints

Through the process of natural selection, natural products possess a unique and vast chemical diversity and have been optimized for specific interactions with biological macromolecules [12]. These secondary metabolites are not merely metabolic byproducts but are crucial for environmental interactions, such as defending against fungi, bacteria, and viruses [30]. Their structural diversity enables them to interact optimally with proteins and other biological targets, a property that is exploited both by the producing organisms and, subsequently, by humans for drug discovery [12].

The structural complexity of natural products often makes them highly effective in modulating challenging biological processes, such as protein-protein interactions [12]. For instance, macrocyclic natural products like cyclosporine A and rapamycin create composite surfaces with their binding proteins to facilitate specific macromolecular interactions, a success that underscores their evolutionary refinement [12].

Chemotaxonomic Patterns in the Plant Kingdom

Large-scale analyses of the known phytochemical space have revealed distinct taxonomic patterns. The distribution of secondary metabolites across the plant kingdom is not random but is strongly influenced by evolutionary ancestry [30]. Research has identified hotspot taxonomic clades rich in medicinal plants and characterized secondary metabolites, alongside other clades that remain chemically under-explored [30]. This phylogenetic conservation occurs because secondary metabolites are typically produced by conserved metabolic routes [30]. The resulting chemical relatedness among species allows for the construction of a chemotaxonomy—a classification system based on chemical similarity—which shows a significant concordance with modern phylogenetic taxonomy [30].

Table 1: Key Categories of Secondary Metabolites and Their Chemotaxonomic Significance

Metabolite Class	Chemotaxonomic Utility	Research Techniques	Example
Phenolics	High value for differentiating dicotyledons and monocotyledons [28].	Spectrophotometry, Chromatography [28].	Flavone glycosides in Citrus species [29].
Non-Protein Amino Acids & Amines	Provide information from chemotaxonomic to severely practical applications [27].	Chromatography, Electrophoresis [28].	Specific amines in the Tephrosieae tribe [28].
Alkaloids	"Privileged" scaffolds with distribution often limited to specific families or genera [12].	LC-MS, NMR [29].	Sugar-shaped alkaloids acting as glycosidase inhibitors [28].
Terpenoids	Useful markers at familial and generic levels, contributing to ecological interactions [28].	GC-MS, LC-MS [29].	---

Analytical Methodologies and Experimental Protocols

The evolution of chemosystematics has been inextricably linked to advancements in analytical instrumentation. The field has progressed from simple chemical tests to sophisticated metabolic profiling, or metabolomics, which captures a comprehensive analysis of small molecule metabolites [27] [29].

Metabolomic Workflow for Chemotaxonomic Classification

A typical workflow for a chemotaxonomic study using metabolomics is outlined below. This protocol is adapted from studies on closely-related Citrus fruits used in Traditional Chinese Medicines [29].

1. Sample Preparation:

• Plant Material Selection: Identify and collect biological material from species of interest across defined taxonomic groups, developmental stages, or environmental conditions. For Citrus studies, this involved sampling fruits at different ripening stages [29].
• Extraction: Homogenize plant tissue and extract metabolites using a suitable solvent system (e.g., methanol-water). Centrifuge to remove particulate matter and pass the supernatant through a solid-phase extraction (SPE) cartridge if necessary for cleanup [29].

2. Instrumental Analysis via UPLC-Q-TOF-MS:

• Chromatographic Separation: Employ Ultra-Performance Liquid Chromatography (UPLC) with a reversed-phase column. A typical mobile phase consists of methanol and 0.1% acetic acid in water, using a gradient elution to achieve optimal separation of metabolites [29].
• Mass Spectrometric Detection: Utilize a Quadrupole-Time-of-Flight (Q-TOF) mass spectrometer for accurate mass detection. Optimized parameters should include [29]:
  • Capillary Voltage: 3.0 kV (positive mode) / 3.5 kV (negative mode)
  • Desolvation Gas Flow: 800 L/h
  • Desolvation Temperature: 400 °C
  • Nebulizer Pressure: 40 psi
• Quality Control (QC): Prepare a pooled QC sample by combining an aliquot of every sample. Run multiple QC samples at the beginning of the sequence and intersperse them after every five analytical samples to monitor system stability [29].

3. Data Processing and Metabolite Identification:

• Data Extraction: Process raw MS data using software to perform peak picking, alignment, and deconvolution, generating a data matrix of features (mass-retention time pairs) and their intensities.
• Metabolite Annotation: Identify metabolites by matching accurate mass and MS/MS fragmentation spectra against authentic standards or curated natural product databases [29]. For unknown compounds, use in-silico fragmentation tools and consider subsequent isolation and NMR spectroscopy for definitive structural elucidation [21].

4. Statistical and Chemometric Analysis:

• Multivariate Analysis: Import the normalized data matrix into statistical software. Use unsupervised methods like Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) to observe natural clustering. Apply supervised methods like Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA) to identify metabolites most responsible for the discrimination between predefined groups [29].
• Biomarker Identification: From the OPLS-DA model, identify potential chemotaxonomic markers based on their variable importance in projection (VIP) scores and confirm significance with univariate statistics [29].
• Pathway Analysis: Input the identified discriminant metabolites into pathway analysis tools (e.g., MetaboAnalyst) to visualize their positions in biochemical pathways and interpret the metabolic basis for the observed classification [29].

The Research Toolkit: Essential Reagents and Materials

Table 2: Key Research Reagent Solutions for Metabolomics-Based Chemosystematics

Item/Reagent	Function in Protocol	Specific Example / Note
UPLC-Q-TOF-MS System	High-resolution separation and accurate mass detection of complex metabolite extracts.	Enables untargeted profiling and preliminary identification of hundreds of compounds [29].
Methanol & Acetic Acid	Components of the mobile phase for chromatographic separation.	Methanol and 0.1% acetic acid provide good peak shape and separation for diverse metabolites [29].
Solid-Phase Extraction (SPE) Cartridges	Clean-up and pre-concentration of plant extracts to remove interfering compounds.	Used prior to injection to protect the chromatographic column and improve data quality.
Authentic Chemical Standards	Validation and absolute quantification of identified metabolites.	Critical for unambiguous annotation of compounds like specific flavone glycosides [29].
Deuterated Solvents (e.g., D₂O, CD₃OD)	Solvents for Nuclear Magnetic Resonance (NMR) spectroscopy.	Used for definitive de novo structure elucidation of novel compounds [21].
Quality Control (QC) Pooled Sample	Monitors instrument stability and performance throughout the analytical sequence.	A pool of all study samples analyzed repeatedly; RSD values for peak areas should be <10% [29].
Kasugamycin hydrochloride	Kasugamycin hydrochloride, CAS:19408-46-9, MF:C14H26ClN3O9, MW:415.82 g/mol	Chemical Reagent
Curvulin	Curvulin, CAS:19054-27-4, MF:C12H14O5, MW:238.24 g/mol	Chemical Reagent

Applications in Chemical Biology and Drug Discovery

The theoretical basis of chemosystematics has profound practical implications, particularly in the discovery and development of new therapeutic agents.

Navigating Chemical Space for Drug Leads

Natural products occupy a unique and vast region of chemical space, distinct from that covered by synthetic combinatorial libraries [12]. This diversity is a direct result of evolutionary selection for biological activity. Analysis of the known phytochemical space reveals that while medicinal plants have been a primary source of drugs, non-medicinal plants also contain numerous bioactive compounds and do not occupy distinct chemical regions [30]. This suggests that chemosystematics can guide the targeted exploration of under-studied taxonomic clades for new drug leads [30]. Historically, natural products and their derivatives have been a major source of new pharmacotherapies, especially for cancer and infectious diseases, with 13 natural product-derived drugs approved worldwide between 2005 and 2007 alone [12].

From Natural Product to Probe and Drug

Numerous natural products have served as essential molecular probes to decipher biological pathways, thereby validating their utility in chemical biology and their inherent bioactivity [12].

• TNP-470: A synthetic analogue of fumagillin, which was identified as a potent inhibitor of angiogenesis. Its target, the type 2 methionine aminopeptidase (MetAP2), was discovered through a combination of chemical modification and site-directed mutagenesis, revealing a new potential therapeutic target for cancer [12].
• FTY720: A synthetic analogue of myriocin, this immunosuppressant is phosphorylated in vivo and acts as an agonist for sphingosine 1-phosphate (S1P) receptors. This reverse pharmacology approach provided novel insights into the S1P pathway and its therapeutic relevance [12].
• Diazonamide A: This marine natural product was found to induce M-phase arrest, not by binding tubulin, but through an unexpected interaction with the mitochondrial enzyme ornithine delta-amino transferase (OAT), revealing a paradoxical role for OAT in cell division [12].

The following diagram illustrates the conceptual journey from a naturally occurring chemotaxonomic marker to a tool for basic research or a clinical therapeutic.

Chemosystematics provides a powerful theoretical and practical framework that bridges evolutionary biology and chemistry. Its core premise—that the chemical constituents of an organism are a reflection of its evolutionary history and genetic makeup—has been validated by modern metabolomic technologies and large-scale analyses of the phytochemical kingdom [29] [30]. The field has evolved from a descriptive cataloguing of compounds to a sophisticated science that can predict taxonomic relationships, reveal biosynthetic pathways, and guide the discovery of new bioactive molecules [27] [12]. As analytical techniques continue to advance, allowing for deeper and more comprehensive metabolic profiling, the integration of chemosystematic data with genomic and transcriptomic information will offer an increasingly holistic view of organismal phylogeny and function. For researchers in chemical biology and drug development, the chemosystematic approach offers a rational, evolutionarily-grounded strategy for navigating the vast, untapped potential of natural products, ensuring its continued relevance in the discovery of new therapeutic agents and biological probes.

Toolkit for the Modern Explorer: Integrating Omics, Synthesis, and Bioengineering

Genome Mining and Biosynthetic Gene Cluster (BGC) Analysis for Prioritizing Discovery

The field of natural product discovery has been revolutionized by the advent of genome mining, a computational approach that leverages the growing wealth of genomic data to identify biosynthetic gene clusters (BGCs). These clusters are chromosomal loci containing genes that encode the biosynthesis of specialized metabolites, which are not essential for growth but provide competitive advantages to producing organisms [31]. Early natural product discovery relied heavily on phenotypic screening of fermentation broths, an approach hampered by high rediscovery rates and low throughput [31]. Genome mining has emerged as a powerful alternative, enabling systematic exploration of an organism's metabolic potential through in silico analysis [31] [32].

BGCs typically contain core biosynthetic genes (such as polyketide synthases [PKS] and non-ribosomal peptide synthetases [NRPS]) that determine the structural scaffold of the metabolite, along with tailoring enzymes (e.g., methyltransferases, oxidoreductases) that modify the core structure, regulatory genes, and transport-related genes [33] [31]. The fundamental premise of genome mining is that identifying and analyzing these clusters can predict an organism's capacity to produce specific secondary metabolites, thus enabling prioritization of strains for further experimental investigation [32]. This approach is particularly valuable for uncovering "cryptic" BGCs—those not expressed under standard laboratory conditions—which represent a vast reservoir of novel chemical diversity [32].

Within chemical biology and systematics research, genome mining provides a phylogenetic framework for understanding metabolic capability across taxa. Large-scale comparative analyses reveal how BGCs are distributed across related species, informing both evolutionary studies and targeted discovery efforts [31]. For drug development professionals, this approach offers a rational strategy to prioritize the most promising BGCs for experimental characterization, streamlining the natural product discovery pipeline.

Core Methodologies and Workflows

Computational Identification of BGCs

The initial step in any genome mining pipeline involves the comprehensive identification of BGCs within genomic data. This process relies on specialized bioinformatics tools and databases that can detect signature sequences of biosynthetic enzymes. antiSMASH (antibiotics & Secondary Metabolite Analysis Shell) stands as the most widely used tool for this purpose, employing rule-based algorithms to identify known BGC classes and predict their chemical products [32]. Other notable tools include PRISM, which specializes in predicting the chemical structures of ribosomal peptides and polyketides, and ClustScan, which offers curated rule-based detection [32].

Recent advances have integrated machine learning approaches to overcome limitations of rule-based methods, particularly for novel BGC classes. Deep learning models like DeepBGC and DECIPHER can identify BGCs based on sequence features without relying exclusively on predefined rules, significantly expanding the discovery space [32]. These tools are trained on characterized BGCs from databases such as MIBiG (Minimum Information about a Biosynthetic Gene Cluster), a curated repository of experimentally validated BGCs [31].

Following identification, BGCs are often grouped into Gene Cluster Families (GCFs) based on sequence similarity, enabling researchers to prioritize clusters by novelty and taxonomic distribution [31]. This phylogenetic framing allows systematic researchers to identify BGCs with restricted taxonomic distributions—potential markers for chemotaxonomic studies—or those conserved across taxa, which may produce metabolites with fundamental ecological functions.

Regulation-Guided Prioritization Strategies

An emerging paradigm in BGC prioritization leverages transcriptional regulatory networks to infer ecological function and therapeutic potential. This innovative approach connects BGCs to specific physiological responses through their regulatory context, providing a third dimension for prioritization alongside traditional genomic and phenotypic screening [34].

The methodology involves genome-wide prediction of transcription factor binding sites (TFBS) using position weight matrices, followed by construction of gene regulatory networks that map relationships between regulators and BGCs [34]. When BGCs co-occur with TFBS for regulators that respond to specific environmental signals (e.g., iron limitation, oxidative stress), they can be functionally associated with the corresponding physiological response. For example, BGCs regulated by iron-responsive factors often encode siderophores, while those controlled by antibiotic response regulators may produce antimicrobial compounds [34].

Integration with gene co-expression networks further strengthens functional predictions. BGCs that are co-expressed with genes of known function under specific conditions can be prioritized for their likely ecological roles or bioactivities [34]. This regulation-guided strategy proved successful in Streptomyces coelicolor, where it identified a novel operon essential for desferrioxamine B biosynthesis that had escaped detection by conventional genome mining tools [34].

Experimental Validation Workflows

Computational predictions require experimental validation to confirm BGC function and characterize the resulting metabolites. A standard workflow encompasses heterologous expression, chemical analysis, and bioactivity testing, as detailed below.

Heterologous expression involves transferring the entire BGC into a model host organism (e.g., S. coelicolor or Aspergillus nidulans for fungal BGCs) optimized for metabolite production [33] [34]. This approach activates silent BGCs and simplifies purification by separating the target metabolite from the native background metabolism. Following expression, metabolite extraction using organic solvents captures the produced compounds, which are then subjected to chemical analysis via liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy for structure elucidation [33]. Finally, bioactivity testing evaluates therapeutic potential through antimicrobial, cytotoxic, or target-specific assays.

Key Research Reagents and Computational Tools

The genome mining workflow relies on specialized computational resources and experimental reagents. The table below summarizes essential components for BGC analysis and characterization.

Table 1: Essential Research Reagents and Computational Tools for BGC Discovery

Category	Resource/Tool	Function	Application Context
BGC Databases	MIBiG [31]	Repository of experimentally characterized BGCs	Reference for BGC annotation and validation
	AntiSMASH DB [32]	Comprehensive database of predicted BGCs	BGC mining and comparative analysis
	BiG-FAM [32]	Database of BGC gene cluster families	GCF-based prioritization and diversity studies
Prediction Tools	antiSMASH [31] [32]	Rule-based BGC detection and analysis	Initial BGC identification in genomic data
	DeepBGC [32]	Machine learning-based BGC prediction	Discovery of novel BGC classes
	PRISM [32]	Chemical structure prediction for RiPPs and polyketides	Structural forecasting from genomic data
Experimental Reagents	Heterologous host systems [33]	Optimized chassis for BGC expression	Activation and production of cryptic BGCs
	LC-MS/MS instrumentation [33]	High-resolution metabolite analysis	Detection and characterization of BGC products
	NMR spectroscopy [33]	Structural elucidation of purified compounds	Determination of chemical structure
5-Hexenyltrichlorosilane	5-Hexenyltrichlorosilane, CAS:18817-29-3, MF:C6H11Cl3Si, MW:217.6 g/mol	Chemical Reagent	Bench Chemicals
1-Isomangostin	1-Isomangostin, CAS:19275-44-6, MF:C24H26O6, MW:410.5 g/mol	Chemical Reagent	Bench Chemicals

Case Studies in BGC Prioritization

Large-Scale Fungal BGC Analysis

A comprehensive study of Alternaria and related fungi demonstrates the power of genome mining for taxonomic insights and risk assessment. Researchers analyzed 6,323 BGCs from 187 genomes, identifying an average of 34 BGCs per genome, with distinct patterns across taxonomic sections [31]. The BGCs were grouped into 548 Gene Cluster Families (GCFs), revealing that sections Infectoriae and Pseudoalternaria possessed highly unique GCF profiles compared to other Alternaria sections [31].

Table 2: Distribution of BGC Classes Across Alternaria Genomes

BGC Class	Average Number Per Genome	Taxonomic Sections with Highest Abundance	Key Metabolites
Polyketide Synthases (PKS)	Not specified	Sections Alternaria and Porri	Alternariol (AOH), Alternariol monomethyl ether (AME)
Non-Ribosomal Peptide Synthetases (NRPS)	Not specified	Sections Infectoriae and Pseudoalternaria	Unknown metabolites with potential diagnostic value
Hybrid PKS-NRPS	Not specified	Distributed across multiple sections	Structural diverse hybrids
Terpenes	Not specified	Not specified in study	Various terpenoid compounds

This analysis enabled targeted food safety recommendations, as the GCF for the mycotoxin alternariol (AOH) was found primarily in Alternaria sections Alternaria and Porri, suggesting these sections should be prioritized for monitoring [31]. Additionally, the study confirmed the presence of AK-toxin I BGC in A. gaisen, supporting phytosanitary regulations regarding this pear pathogen [31]. The unprecedented scale of this analysis—spanning 123 Alternaria and 64 related genomes—showcases how genome mining can inform both natural product discovery and applied regulatory science.

Regulation-Based Discovery in Streptomyces

The integration of regulatory network analysis with genome mining enabled the discovery of novel genes involved in desferrioxamine B biosynthesis in Streptomyces coelicolor [34]. By mapping the regulon of the iron master regulator DmdR1 and analyzing co-expression patterns, researchers identified the desJGH operon, which had escaped detection by conventional genome mining tools [34].

Experimental validation through gene deletion confirmed the functional role of desJGH in desferrioxamine B biosynthesis, with deletion mutants showing strongly reduced production [34]. This case study illustrates how regulation-based prioritization can uncover hidden components of known metabolic pathways and identify BGCs with predicted ecological functions based on their regulatory context.

Integration with Chemical Biology and Systematics

Genome mining bridges chemical biology and systematics by establishing direct connections between genomic capacity, metabolic output, and taxonomic classification. The distribution patterns of BGCs and GCFs across phylogenetic trees provide chemical systematists with valuable markers for refining taxonomic classifications and understanding evolutionary relationships [31]. For example, the unique GCF profiles of Alternaria sections Infectoriae and Pseudoalternaria reinforce their phylogenetic distinctness and support their recognition as evolutionarily significant lineages [31].

From a chemical biology perspective, genome mining illuminates the biochemical potential encoded in microbial genomes, enabling targeted discovery of enzymes with novel catalytic functions [33]. Tailoring enzymes, such as methyltransferases identified through genome mining, represent valuable biocatalysts for synthetic biology applications [33]. The systematic identification of BGCs encoding specific enzyme classes facilitates the development of enzyme libraries for combinatorial biosynthesis and metabolic engineering.

For drug development professionals, the integration of genome mining with chemical systematics enables evidence-based prioritization of microbial strains for screening programs. By focusing on taxonomic groups with high BGC diversity or unique GCF profiles, researchers can maximize the probability of discovering novel bioactive compounds while minimizing rediscovery of known metabolites. This approach represents a significant advancement over traditional activity-guided screening, offering both efficiency gains and deeper insights into the ecological context of specialized metabolism.

The escalating demand for sustainable bio-based production of chemicals and therapeutics has intensified the need for accelerated biological design cycles. Cell-free synthetic biology emerges as a powerful platform that decouples pathway construction from the constraints of cell viability, offering an open and controllable environment for prototyping biosynthetic pathways. This technical guide details the core principles, methodologies, and applications of cell-free systems, with a specific focus on their transformative role in the discovery and optimization of natural product biosynthesis. By enabling high-throughput, automated Design-Build-Test-Learn (DBTL) cycles, cell-free prototyping significantly accelerates the engineering of microbial cell factories, positioning it as an indispensable tool for researchers and drug development professionals in the field of chemical biology and systematics research.

The sustainable production of high-value natural products, such as medicines and biofuels, faces a significant bottleneck: the long research and development timelines, often spanning 10 to hundreds of person-years, required to engineer functional microbial cell factories [35]. This challenge is rooted in the inherent complexity of living systems, where cellular growth objectives and metabolic overhead often conflict with engineering goals. Cell-free synthetic biology circumvents these limitations by leveraging the catalytic machinery of the cell without the intact, living entity.

Cell-free systems are in vitro platforms based on crude cell lysates or purified recombinant elements that perform transcription, translation, and metabolism [36]. This "bottom-up" approach provides a unique set of advantages for pathway prototyping and natural product biosynthesis:

• Open System: Allows direct manipulation of the reaction environment, including substrate concentration, pH, and cofactors, enabling the synthesis of products toxic to living cells [35].
• Direct Control: Eliminates the cellular membrane barrier, granting immediate access to the reaction milieu for real-time monitoring and control.
• Decoupled from Growth: Focuses the system's energy and resources solely on the desired biosynthetic objective, often leading to higher yields and productivities [35].
• High-Throughput Capability: The simplicity of the system makes it ideally suited for automation and parallelization, drastically accelerating the Design-Build-Test-Learn (DBTL) cycle [37].

Framed within the broader context of natural product research, cell-free systems provide a systematic and efficient platform for exploring the vast chemical diversity encoded in biological systems, from elucidating biosynthetic gene cluster functions to rapidly optimizing production pathways for drug development.

Core Cell-Free Platforms: Lysates and Defined Systems

Two broad classes of cell-free systems dominate in vitro small-molecule synthesis, each with distinct advantages and ideal application niches [35]. The table below provides a structured comparison for easy evaluation.

Table 1: Comparison of Major Cell-Free Platform Types

Feature	Crude Lysate-Based Systems	Defined (Purified) Systems (e.g., PURE)
Composition	Ensemble of biocatalysts from cell lysates; contains native metabolism.	Defined set of purified components (e.g., 36 proteins, tRNAs, ribosomes) [36].
Key Advantages	Lower catalyst cost; inherent cofactor regeneration; native-like metabolic support [35].	Precise, defined composition; no proteases or nucleases; highly flexible and modular [36].
Typical Applications	High-yield production of metabolites (e.g., 2,3-butanediol, n-butanol); pathway debugging [35].	Synthesis of toxic proteins; incorporation of non-natural amino acids; mechanistic studies [36].
Throughput & Scalability	High-throughput prototyping; generally easier to scale for biomanufacturing [36].	Ideal for small-scale, high-throughput synthesis and screening [36].

The choice between these systems depends on the project's primary goal. Crude lysates are often preferred for complex metabolic engineering and cost-effective biomanufacturing, whereas defined systems are superior for applications requiring precision, control, and the incorporation of non-standard biological parts.

Experimental Framework and Protocols

The cell-free metabolic engineering (CFME) framework is a practical implementation of the DBTL paradigm, enabling rapid pathway construction and testing.

The Design-Build-Test-Learn (DBTL) Cycle

The following diagram illustrates the iterative DBTL cycle, central to modern synthetic biology and enhanced by cell-free platforms.

Key Experimental Protocols

A. S12 Lysate Preparation for E. coli-Based Systems

A high-yielding, high-throughput method for preparing foundational crude lysates [35].

• Growth and Harvest: Grow an overnight culture of the chosen chassis strain (e.g., E. coli). Dilute the culture into fresh, rich medium and incubate with shaking until mid-log phase. Chill the culture rapidly and harvest cells via centrifugation.
• Cell Washing: Resuspend the cell pellet in a cold buffer solution and centrifuge again. This step removes residual growth media.
• Lysis: Resuspend the final cell pellet in a lysis buffer containing key metabolites. Pass the suspension through a high-pressure homogenizer (e.g., French Press or similar) to lyse the cells.
• Clarification: Centrifuge the lysate at high speed to remove cell debris and genomic DNA. The resulting supernatant is the active S12 extract, which should be aliquoted, flash-frozen in liquid nitrogen, and stored at -80°C.

Diagram: Lysate Preparation and Pathway Assembly Workflow

B. Mix-and-Match Cell-Free Metabolic Engineering

This approach constructs pathways by combining lysates from different chassis strains, each pre-engineered to overexpress a single heterologous enzyme [35].

• Individual Enzyme Expression: Engineer separate E. coli strains, each overproducing one enzyme of the target biosynthetic pathway.
• Individual Lysate Preparation: Prepare S12 lysates from each of these specialized strains using the protocol above.
• Pathway Assembly: Combine the individual lysates in a single reaction vessel, supplemented with necessary substrates, cofactors, and energy sources.
• Reaction Incubation: Incubate the master mix at the optimal temperature and monitor product formation over time.

C. Cell-Free Protein Synthesis-Driven Metabolic Engineering

This method leverages the cell-free system's own transcription-translation machinery to produce pathway enzymes in situ from added DNA templates, enabling ultra-rapid prototyping [35].

• Template Preparation: Clone genes encoding the pathway enzymes into plasmids under the control of a promoter compatible with the cell-free system.
• Reaction Assembly: Add these DNA templates directly to the standard cell-free reaction mixture.
• One-Pot Synthesis and Production: During incubation, the cell-free system simultaneously expresses the enzymes and carries out the biosynthetic pathway, converting added substrates into the target natural product.

Applications in Natural Product Biosynthesis and Sensing

Cell-free systems are revolutionizing several key areas within natural product research and chemical biology.

• Pathway Prototyping and Debugging: CFME allows for the rapid construction and testing of hundreds of pathway variants in a single week, identifying rate-limiting steps and enzyme incompatibilities before committing to lengthy in vivo engineering [35]. This is invaluable for activating and characterizing cryptic biosynthetic gene clusters.
• Biosensor Development for Real-Time Monitoring: Cell-free biosensors are being engineered for highly sensitive detection of small molecules. For instance, the ROSALIND platform has been integrated with genetic circuitry that acts as a signal amplifier, detecting environmental contaminants with 10-fold greater sensitivity [36]. Furthermore, platforms incorporating genetic code expansion can rapidly evolve fluorescent nanosensors for low-cost, real-time disease monitoring [36].
• Synthesis of "Difficult-to-Express" Products: The open nature of cell-free systems enables the production of proteins and natural products that are toxic to host cells, such as certain antibiotics and cytotoxic anticancer agents [37].

Integration with Advanced Technologies

The true power of cell-free prototyping is unlocked when integrated with automation and machine learning.

Table 2: The Scientist's Toolkit: Key Reagents and Technologies

Item	Function/Description	Application in Cell-Free Systems
PUREfrex Kit	A commercial defined (PURE) cell-free protein synthesis system [36].	Synthesis of antibodies, membrane proteins, and for incorporation of unnatural amino acids.
NEBExpress / PURExpress	Commercial crude lysate-based and defined cell-free protein synthesis kits [36].	Robust, off-the-shelf systems for protein expression and pathway prototyping.
Automated Recommendation Tool (ART)	A machine learning tool that uses Bayesian modeling to recommend optimal strain designs from experimental data [38].	Guides the "Learn" phase of the DBTL cycle, predicting high-producing strains for the next round of testing.
Active Learning & AI Optimization	AI algorithms used to explore a vast combinatorial space of cell-free buffer compositions [36].	Dramatically increases protein production; identifies critical parameters for cell-free productivity.
Microfluidic Biochips	Miniaturized devices for handling small fluid volumes.	Enables massive parallelization of cell-free reactions for high-throughput screening [37].

The integration of machine learning, particularly through tools like the Automated Recommendation Tool (ART), is transforming the "Learn" phase of the DBTL cycle. ART leverages probabilistic modeling on often sparse experimental data to recommend which strain or pathway variant to build and test next, effectively guiding the bioengineering process towards optimal production [38]. When combined with automated high-throughput data generation, this creates a powerful, self-improving engineering loop.

Cell-free synthetic biology has evolved from a basic biological tool into a sophisticated platform for bottom-up design and prototyping. By providing an open, controllable, and highly scalable environment, it addresses critical bottlenecks in the engineering of natural product biosynthetic pathways. The integration of modular cell-free systems with automation, machine learning, and advanced biosensor design promises to further compress development timelines. For researchers in chemical biology and systematics, the adoption of cell-free methodologies offers a systematic and accelerated path from genetic sequence to functional natural product, paving the way for faster discovery and development of novel biofuels, medicines, and chemicals.

Mass spectrometry-based metabolomics has emerged as an indispensable tool in chemical biology and systematics research, enabling the comprehensive analysis of small molecules in biological systems. As the endpoint of the "omics cascade," metabolomics provides a direct readout of cellular phenotype and physiological status, positioning it closer to the observable biological characteristics than genomics, transcriptomics, or proteomics [39]. In the context of natural products research, this approach is particularly valuable for studying the complex chemical profiles of organisms and streamlining the discovery of bioactive compounds. The field aims to identify and quantify wide arrays of metabolites with diverse physicochemical properties that occur at different abundance levels, presenting significant analytical challenges [39]. Within natural product chemistry, metabolomics constitutes a powerful strategy to accelerate the classic and laborious process of isolating natural products, which often involves the re-isolation of known compounds [40]. By integrating advanced mass spectrometry with sophisticated data analysis, researchers can now navigate the complex chemical space of natural products more efficiently, focusing resources on novel compounds with desired biological activities.

Core Concepts and Approaches in Metabolomics

Key Methodological Frameworks

Metabolomics investigations primarily employ two complementary approaches: metabolic profiling and metabolic fingerprinting, each with distinct objectives and applications [39]. Metabolic profiling focuses on the quantitative analysis of a predefined set of metabolites, either related to a specific metabolic pathway or belonging to a particular class of compounds. This hypothesis-driven approach often targets specific biomarkers of disease, toxicant exposure, or substrates and products of enzymatic reactions. The results are quantitative and ideally independent of the analytical technology, enabling the construction of databases that can be integrated with pathway maps or other omics data [39]. In contrast, metabolic fingerprinting represents an unbiased, global screening approach to classify samples based on metabolite patterns or "fingerprints" that change in response to disease, environmental, or genetic perturbations. Initially, this method does not aim to identify every observed metabolite but rather to compare patterns that differentiate sample classes, with the ultimate goal of identifying and validating the discriminating metabolites [39]. A related technique, metabolic footprinting, analyzes extracellular metabolites in cell culture media as a reflection of metabolite excretion or uptake by cells, providing valuable information on cellular phenotype and physiological state [39].

Analytical Platforms and Their Applications

The choice of analytical platform is critical in metabolomics study design, with mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy serving as the primary technologies. MS-based metabolomics is typically coupled with separation techniques such as liquid chromatography (LC) or gas chromatography (GC), which reduce sample complexity and allow sequential analysis of different molecular sets [41]. LC-MS is particularly suitable for detecting moderately polar to highly polar compounds, including fatty acids, alcohols, phenols, vitamins, organic acids, polyamines, nucleotides, polyphenols, terpenes, and flavonoids [41]. GC-MS detects volatile compounds or those that can be derivatized into volatile forms, making it ideal for amino acids, organic acids, fatty acids, sugars, polyols, amines, and sugar phosphates [41]. The inherent advantage of MS lies in its high sensitivity, ability to characterize chemical structures through fragmentation patterns, and compatibility with small sample volumes [40]. NMR spectroscopy, while less sensitive than MS, offers distinct benefits as a nondestructive and highly reproducible technique that requires minimal sample preparation and provides rich structural information [41]. The application of high-resolution magic angle spinning (HRMAS) NMR spectroscopy further extends these capabilities to intact tissue samples, preserving valuable biological material for additional analyses [41].

Table 1: Comparison of Major Analytical Platforms in Metabolomics

Platform	Key Advantages	Common Applications	Technical Considerations
LC-MS	High sensitivity; broad metabolite coverage; minimal sample derivation	Polar to moderately polar compounds; lipids; secondary metabolites	May require method optimization for different compound classes
GC-MS	High chromatographic resolution; excellent reproducibility; robust compound identification	Volatile compounds; organic acids; sugars; amino acids (after derivation)	Requires derivation for non-volatile compounds; limited to thermally stable molecules
NMR	Non-destructive; quantitative; provides structural information; minimal sample preparation	Intact tissue analysis (via HRMAS); metabolic flux studies; absolute quantification	Lower sensitivity compared to MS; higher sample requirement

Experimental Design and Workflow

A well-considered experimental design is fundamental to successful metabolomics investigations, particularly given the high temporal and spatial variability of metabolite distributions and confounding factors such as circadian fluctuations in mammalian organisms and diet-dependent biological variability [39]. The metabolomics workflow follows a structured pathway from sample preparation to biological interpretation, with each stage requiring careful execution to ensure data quality and reliability.

Sample Preparation and Quality Control

Proper sample preparation is critical for generating reliable metabolomics data. The specific protocols vary significantly depending on the biological matrix (tissues, biofluids, cell cultures), the analytical platform, and the classes of metabolites of interest. For MS-based analyses, sample preparation typically involves protein precipitation, metabolite extraction using appropriate solvents, and concentration steps to ensure optimal detection of metabolites across different abundance ranges [39]. Quality control (QC) samples are essential throughout the process to monitor technical variability and ensure analytical robustness. These QC samples are used to balance the analytical platform's bias, correct for signal noise, and determine the variance of metabolite features [41]. Features with excessive variance are typically removed from subsequent analysis to enhance data quality. The incorporation of internal standards, both stable isotope-labeled and chemical analogs, further strengthens quantitative accuracy and enables correction for matrix effects and instrument variability.

Data Acquisition Parameters

Data acquisition parameters must be optimized for the specific research question and analytical platform. For untargeted metabolomics using high-resolution mass spectrometry, parameters should ensure broad metabolite coverage while maintaining data quality. Key considerations include mass resolution (typically >30,000 for untargeted analysis), mass accuracy (<5 ppm error), scan speed, and dynamic range [41]. For LC-MS applications, chromatographic conditions must be optimized to achieve sufficient separation of complex metabolite mixtures, with typical reverse-phase methods employing water/acetonitrile or water/methanol gradients with modifiers such as formic acid or ammonium acetate to enhance ionization [41]. In GC-MS analyses, derivatization (typically using silylation reagents) is necessary for most metabolites to ensure volatility and thermal stability, with temperature-programmed separations providing the resolution needed for complex samples [41]. Data-dependent acquisition (DDA) methods are commonly employed to obtain MS/MS fragmentation data for compound identification, while data-independent acquisition (DIA) approaches provide comprehensive fragmentation data for all detectable ions, albeit with greater complexity in data interpretation [42].

Data Processing and Statistical Analysis

Preprocessing and Metabolite Identification

Raw data from mass spectrometry experiments require extensive preprocessing to extract meaningful biological information. This process typically involves noise reduction, retention time correction, peak detection and integration, and chromatographic alignment using specialized software tools such as XCMS, MAVEN, or MZmine [41]. Following preprocessing, data normalization is essential to reduce systematic bias or technical variation, with methods ranging to total ion current normalization and probabilistic quotient normalization to more advanced algorithms that account for sample dilution and matrix effects [41]. Compound identification represents a significant challenge in metabolomics, with the Metabolomics Standards Initiative (MSI) establishing four levels of confidence: identified metabolites (level 1), presumptively annotated compounds (level 2), presumptively characterized compound classes (level 3), and unknown compounds (level 4) [41]. Identification typically involves matching experimental data to authentic standards in in-house libraries or public databases, with accurate mass, isotopic pattern, retention time, and fragmentation spectrum providing complementary evidence for confident annotation.

Statistical Analysis and Data Integration

Statistical analysis in metabolomics encompasses both unsupervised and supervised methods to extract biologically meaningful patterns from complex datasets. Unsupervised methods such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) explore inherent data structure without prior knowledge of sample classes, helping to identify outliers, batch effects, and natural groupings within the data [40]. Supervised methods like partial least squares-discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) incorporate class information to maximize separation between predefined groups and identify features most responsible for these distinctions [42]. These approaches are particularly valuable for biomarker discovery and for understanding metabolic perturbations associated with disease states or therapeutic interventions. For comprehensive biological interpretation, metabolic pathway analysis and metabolite set enrichment analysis (MSEA) place statistically significant metabolites in the context of known biochemical pathways, helping researchers identify affected biological processes and generate testable hypotheses [42].

Table 2: Essential Bioinformatics Tools for Metabolomics Data Analysis

Tool/Platform	Primary Function	Key Features	Application in Dereplication
MetaboAnalyst	Statistical analysis and functional interpretation	Comprehensive suite for univariate and multivariate statistics; pathway analysis; biomarker analysis	Identifies features differentiating active/inactive samples through sPLS and other methods [40]
GNPS	Tandem MS data analysis and molecular networking	Community-wide platform for MS/MS spectral matching; molecular networking; analog discovery	Clusters related compounds; facilitates dereplication through database matching [40]
XCMS/MZmine	Raw data preprocessing	Peak detection; retention time alignment; peak integration; compound quantification	Generates feature tables for statistical analysis; essential preprocessing step [41]
NP-MRD	Natural product database	Open-access database containing NMR spectra and structure data for known natural products	Dereplication through spectral matching; identification of known compounds [43]

Dereplication Strategies in Natural Products Research

Integrating Metabolomics with Bioactivity Screening

Dereplication—the early identification of known compounds in complex mixtures—represents a critical challenge in natural products research, where the rediscovery of previously characterized molecules can consume significant resources without advancing knowledge. Mass spectrometry-based metabolomics provides powerful solutions to this challenge by enabling correlation of chemical features with biological activity prior to isolation. A demonstrated workflow involves preparing extracts from various biological sources (e.g., different plant parts), fractionating these extracts to increase chemical diversity, subjecting fractions to bioactivity screening, and then applying metabolomics approaches to identify compounds responsible for the observed activity [40]. In a study on Annona crassiflora, for example, fractions with larvicidal activity against Aedes aegypti were distinguished from inactive fractions using both LC-MS data analyzed in MetaboAnalyst and LC-MS/MS data processed through GNPS, successfully identifying annonaceous acetogenins as the active compound class [40]. This integrated approach allows researchers to prioritize fractions containing potentially novel bioactive compounds while avoiding the isolation of known entities, significantly accelerating the discovery process.

Advanced Annotation and Molecular Networking

Molecular networking via the GNPS platform has emerged as a particularly powerful tool for dereplication and analog discovery in natural products research. This approach organizes complex MS/MS data based on spectral similarity, grouping structurally related compounds into visual networks that facilitate both dereplication and the discovery of structural analogs [40]. Each node in the network represents a precursor ion with its associated MS/MS spectrum, while edges connecting nodes indicate significant spectral similarity suggestive of structural relationships. The visualization includes pie charts showing the distribution of compounds across different sample groups (e.g., active versus inactive fractions), enabling immediate identification of features associated with bioactivity [40]. When coupled with in silico fragmentation tools and database mining, molecular networking significantly expands the ability to annotate novel compounds that may not be present in existing databases, providing a comprehensive strategy for navigating the chemical space of complex natural product extracts.

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of mass spectrometry-based metabolomics requires carefully selected reagents, materials, and analytical standards to ensure data quality and reproducibility. The following table outlines essential components of the metabolomics toolkit, particularly focused on applications in natural product research and dereplication.

Table 3: Essential Research Reagents and Materials for MS-Based Metabolomics

Category	Specific Examples	Function and Application
Chromatography Solvents	LC-MS grade water, acetonitrile, methanol; HPLC grade chloroform, ethyl acetate	High-purity solvents for metabolite extraction and chromatographic separation to minimize background interference and ion suppression
Derivatization Reagents	N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA); methoxyamine hydrochloride	Chemical modification of metabolites for GC-MS analysis to enhance volatility and thermal stability
Internal Standards	Stable isotope-labeled amino acids, fatty acids, nucleotides; chemical analogs	Correction for technical variability during sample preparation and analysis; quality control for quantitative measurements
Solid Phase Extraction Materials	Diol, C18, polymer-based cartridges	Fractionation of complex extracts to reduce complexity and enrich specific metabolite classes prior to analysis [40]
Quality Control Materials	Pooled quality control samples; NIST reference materials; commercial quality control kits	Monitoring instrument performance; evaluating technical variability; ensuring data quality throughout analytical batches
Authentic Standards	Commercially available metabolite standards; purified natural products	Construction of in-house spectral libraries for confident metabolite identification and quantification
(+)-Menthofuran	(+)-Menthofuran, CAS:17957-94-7, MF:C10H14O, MW:150.22 g/mol	Chemical Reagent
Bromamphenicol	Bromamphenicol	Bromamphenicol is a broad-spectrum antibiotic for research, inhibiting bacterial protein synthesis. For Research Use Only. Not for human consumption.

Future Perspectives and Concluding Remarks

Mass spectrometry-based metabolomics continues to evolve rapidly, with emerging technologies and methodologies enhancing its application in natural products research. The integration of multi-omics approaches—combining metabolomics with genomics, transcriptomics, and proteomics—provides unprecedented opportunities to understand the biological context of metabolic perturbations and identify modes of action for bioactive natural products [41]. Advances in computational methods, including machine learning and artificial intelligence, are improving compound identification and enabling the prediction of metabolite structures from MS/MS spectra with increasing accuracy. Furthermore, the development of open-access databases and collaborative platforms such as the Natural Product Magnetic Resonance Database (NP-MRD) promotes data sharing and community-driven expansion of resources [43]. As these technologies mature, mass spectrometry-based metabolomics will play an increasingly central role in chemical biology and systematics research, enabling more efficient discovery of novel bioactive compounds and enhancing our understanding of biological systems at the molecular level. The continued refinement of dereplication strategies will be particularly valuable for maximizing the efficiency of natural product discovery programs, ensuring that research resources are focused on compounds with the greatest potential for scientific advancement and therapeutic application.

The integration of artificial intelligence (AI) and deep learning (DL) is revolutionizing the prediction of drug-target interactions (DTI), a cornerstone of modern drug discovery. This whitepaper provides an in-depth technical examination of how these computational approaches are creating a quantitative framework for profiling interactions with therapeutic targets, thereby accelerating the identification of novel drug candidates. Framed within the resurgent interest in natural products for their unparalleled chemical diversity and bioactivity, this guide details the core methodologies, from foundational concepts to cutting-edge multimodal architectures. We present structured data, detailed experimental protocols, and essential toolkits to equip researchers with the practical knowledge to leverage AI in expanding the target space, particularly for characterizing the complex mechanisms of natural compounds.

Natural products and their structural analogues have historically been a major source of pharmacotherapies, especially in the realms of cancer and infectious diseases [21]. Their inherent structural complexity and biodiversity offer unique advantages for interacting with challenging therapeutic targets. However, the systematic exploration of their target space has been hampered by technical barriers in screening, isolation, and characterization [21].

The conventional drug discovery process is notoriously inefficient, often taking 10-15 years with a success rate of less than 12% [44]. Within this challenging context, AI has emerged as a transformative force. AI-driven drug discovery (AIDD) can compress development timelines, access previously inaccessible chemical spaces, and predict drug-like compounds with a higher potential to survive clinical attrition [44]. The application of AI for drug-target interaction (DTI) and drug-target affinity (DTA) prediction provides a powerful computational lens through which to study the binding dynamics of natural products, offering strong solutions to these challenging biological problems [45]. This paradigm shift replaces labor-intensive, human-driven workflows with AI-powered discovery engines capable of redefining the speed and scale of modern pharmacology [46].

The Evolution of Deep Learning for DTI/DTA Prediction

The quest to predict drug-target binding has evolved significantly from its early statistical and classical machine learning roots. Early methods relied on manually curated descriptors or features of drugs and targets, which posed a significant challenge as they required in-depth pharmacodynamics knowledge and were susceptible to errors [45].

The paradigm began to shift with the advent of deep learning. DL gained popularity due to its ability to handle large datasets, deliver better performance, and learn intricate non-linear relationships between input data and output, thus diminishing the challenge of manual feature selection [45]. The development can be visualized as a progression of methodological sophistication.

Figure 1. The methodological evolution of AI in drug-target binding prediction, from early statistical approaches to modern multimodal architectures [45].

Methodological Progression

• Early Deep Learning Approaches: Initial models leveraged convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on one-dimensional sequential representations of drugs (e.g., SMILES) and targets (e.g., amino acid sequences). While superior to previous methods, a key limitation was their focus on primary structures, often ignoring three-dimensional configurations and specific binding pocket information [45].
• Graph-Based and Attention-Based Methods: Graph-based methods represented molecules as higher-dimensional graphs, accounting for the positional aspects of constituent atoms. Attention-based approaches, utilizing concepts like multi-headed attention and feature aggregation, provided better results by extracting more complex features relevant to DTI/DTA prediction [45].
• Contemporary Multimodal and LLM-Based Approaches: Recent developments include natural-language-based methods that treat DTB prediction as a hybrid-natural language problem. The development of domain-specific large language models (LLMs) like ChemBERTa and ProtBERT, derived from established models like BERT, is an active research area. These LLMs generate semantic embeddings from chemical structures, which are combined with graph-based and attention-based methods for improved prediction and feature interpretation [45].

Core AI Architectures and Methodologies

Problem Framing: DTI vs. DTA

AI-based approaches for target space prediction primarily address two complementary tasks [45] [47]:

• Drug-Target Interaction (DTI) Prediction: This is typically framed as a qualitative classification problem, predicting whether a given drug-target pair interacts (a binary "0/1" problem) or ranking candidate interactions.
• Drug-Target Affinity (DTA) Prediction: This is a quantitative regression problem, predicting the binding affinity strength (e.g., IC50, Ki, Kd) between a drug and its target, often normalized to a value between 0 and 1.

The performance of AI models is intrinsically linked to the quality and diversity of the input data. Commonly used data types and sources are summarized in the table below.

Table 1: Common Data Sources and Representations for AI-Driven DTI/DTA Prediction

Data Category	Specific Types & Representations	Key Sources & Datasets
Drug/Compound Data	Chemical structure, SMILES strings, molecular graphs, fingerprints.	PubChem, BindingDB, ChEMBL, ZINC [47].
Target/Protein Data	Amino acid sequence (FASTA), 3D structure (PDB), protein contact maps.	Uniprot, Protein Data Bank (PDB), AlphaFold DB [47].
Interaction Data	Known binary DTIs, binding affinity values (Ki, Kd, IC50).	BindingDB, Davis, KIBA, Gold Standard datasets (NR, GPCR, IC, Enzyme) [47] [45].
Auxiliary Data	Disease associations, gene expression, side effects, pharmacological data.	DrugBank, Comparative Toxicogenomics Database (CTD), clinical databases [47].

Key Deep Learning Architectures in Practice

Modern DTI/DTA models often employ sophisticated, multi-component deep-learning architectures.

• Graph Neural Networks (GNNs): GNNs are exceptionally suited for representing molecular structures of drugs as graphs, where atoms are nodes and bonds are edges. They learn features that capture the topological and functional properties of compounds [45] [48].
• Transformers and Attention Mechanisms: Transformer models, with their self-attention mechanisms, can effectively process sequential data like protein sequences and SMILES strings. They weigh the importance of different residues or atoms in the context of the entire molecule, capturing long-range dependencies critical for binding [45] [47].
• Convolutional Neural Networks (CNNs): While initially used for 1D sequences, CNNs are also applied to 2D representations of molecular structures or even 3D structural data of protein binding pockets to extract spatially local features [45].
• Hybrid and Multimodal Models: The most advanced models are hybrid, integrating multiple data types and architectures. For example, a GNN can process the drug molecule while a CNN or Transformer processes the protein sequence, with their learned representations fused through cross-attention or other mechanisms for the final prediction [45].

The following diagram illustrates a typical workflow for a multimodal DTI/DTA prediction model.

Figure 2. A generalized workflow for a multimodal AI model predicting drug-target interactions and affinity, integrating diverse drug and target representations [45] [47].

Experimental Protocols for AI-Driven Target Prediction

This section provides a detailed methodology for building and validating an AI model for DTA prediction, a common task in profiling natural products.

Protocol: Building a Graph-Based DTA Prediction Model

Objective: To predict the continuous binding affinity value between a natural product compound and a specified protein target.

1. Data Curation and Preprocessing

• Source your data: Obtain binding affinity data (e.g., Kd values) from a reliable database like BindingDB [47]. For natural products, cross-reference with sources like the Natural Products Atlas or in-house libraries.
• Process compounds: For each compound, generate a molecular graph representation. Using a toolkit like RDKit, convert the SMILES string into a graph where nodes represent atoms (featurized with atom type, degree, hybridization, etc.) and edges represent bonds (featurized with bond type) [47].
• Process targets: For each protein target, use the amino acid sequence. Alternatively, for a more advanced model, use a predicted or experimentally determined 3D structure from the PDB or AlphaFold DB [47] [49].
• Dataset splitting: Split the dataset into training, validation, and test sets using a stratified split based on the affinity value distribution or a cold split (proteins/compounds in the test set are not seen in training) to assess generalization.

2. Model Architecture Definition A recommended hybrid architecture is the GraphDTA paradigm:

• Drug Encoding Branch: Use a Graph Neural Network (e.g., Graph Convolutional Network (GCN) or Graph Attention Network (GAT)) to process the molecular graph of the compound and generate a fixed-size molecular embedding.
• Target Encoding Branch: Use a 1D Convolutional Neural Network (CNN) or a Transformer to process the amino acid sequence of the protein and generate a fixed-size protein embedding.
• Interaction Prediction Head: Concatenate the drug and target embeddings. Feed the combined vector through a series of fully connected (dense) layers to finally output a single continuous value representing the predicted binding affinity.

3. Model Training and Optimization

• Loss Function: Use Mean Squared Error (MSE) or Mean Absolute Error (MAE) as the loss function, as this is a regression task.
• Optimizer: Use the Adam optimizer with an initial learning rate of 0.001 and implement a learning rate scheduler that reduces the rate upon validation loss plateau.
• Regularization: Apply techniques like Dropout and L2 weight decay to prevent overfitting. Monitor the validation loss throughout training for early stopping.

4. Model Validation and Analysis

• Performance Metrics: Evaluate the model on the held-out test set using metrics like:
  • Mean Absolute Error (MAE)
  • Concordance Index (CI)
  • R^2 (Coefficient of Determination)
• Interpretability: Use post-hoc interpretation methods like Saliency Maps or Integrated Gradients for the target sequence, and visualization tools like GNNExplainer for the molecular graph to identify which atoms or residues the model deems critical for binding.

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 2: Key Research Reagents and Computational Tools for AI-Driven DTI/DTA

Item / Solution	Function / Application	Example / Source
RDKit	An open-source cheminformatics toolkit used for manipulating chemical structures, converting SMILES to graphs, and calculating molecular descriptors.	https://www.rdkit.org [47]
Deep Learning Framework	A programming library used to build, train, and validate complex neural network models.	PyTorch, TensorFlow, JAX
AlphaFold DB	A database of protein structure predictions used to obtain highly accurate 3D structural data for targets with unknown experimental structures.	https://alphafold.ebi.ac.uk [47] [49]
BindingDB	A public, web-accessible database of measured binding affinities, focusing primarily on the interactions of drug-like molecules with their protein targets.	https://www.bindingdb.org [47]
PubChem	A database of chemical molecules and their activities against biological assays, providing a vast resource of compound information and bioactivity data.	https://pubchem.ncbi.nlm.nih.gov [47]
Davis/KIBA Datasets	Curated benchmark datasets specifically for DTA prediction, used for model training and comparative performance benchmarking.	[47]
Amidepsine A	Amidepsine A, MF:C29H29NO11, MW:567.5 g/mol	Chemical Reagent
Deoxynojirimycin Tetrabenzyl Ether	Deoxynojirimycin Tetrabenzyl Ether, CAS:69567-11-9, MF:C34H37NO4, MW:523.7 g/mol	Chemical Reagent

Clinical Translation and Industry Impact

The translational impact of AI in drug discovery is no longer theoretical. By the end of 2024, over 75 AI-derived molecules had reached clinical stages, a remarkable leap from just a few years prior [46]. These candidates are entering trials in a fraction of the traditional ~5-year discovery timeline.

Table 3: Selected AI-Discovered Small Molecules in Clinical Stages (as of 2025)

Small Molecule	Company	Target	Stage	Indication
INS018_055	Insilico Medicine	TNIK	Phase 2a	Idiopathic Pulmonary Fibrosis (IPF) [48]
GTAEXS617	Exscientia	CDK7	Phase 1/2	Solid Tumors [48]
ISM3091	Insilico Medicine	USP1	Phase 1	BRCA mutant cancer [48]
RLY2608	Relay Therapeutics	PI3Kα	Phase 1/2	Advanced Breast Cancer [48]
DSP1181	Exscientia	(Serotonin Receptor)	Phase 1	Obsessive Compulsive Disorder (OCD) [46]

These successes are underpinned by demonstrated efficiency gains. For instance, Exscientia's AI platform has reported design cycles that are ~70% faster and require 10x fewer synthesized compounds than industry norms [46]. In one specific program, a clinical candidate (a CDK7 inhibitor) was achieved after synthesizing only 136 compounds, a figure drastically lower than the thousands typically required in traditional medicinal chemistry [46].

Future Directions and Challenges

Despite rapid progress, the field must overcome several challenges to fully deliver on its promise.

• Data Quality and Imbalance: The known interactions between drugs and targets are significantly sparse compared to unknown interactions, leading to a severe class imbalance problem that challenges model training [47].
• Integration of 3D Structural Information: With the advent of highly accurate protein structure prediction tools like AlphaFold, a key research question is how to best integrate 3D structural data to improve predictive accuracy and model binding mechanisms [47] [49].
• Generative AI for De Novo Design: The field is moving beyond prediction toward generation. Generative AI allows for the de novo design of novel molecular structures from scratch, exploring chemical territories human chemists may not consider [47] [44].
• Explainability and Trust: As models grow more complex, the "black box" problem persists. Developing methods to interpret model predictions and build trust among scientists is critical for widespread adoption [46] [47].
• Leveraging Large Language Models (LLMs): Exploring the powerful reasoning capabilities of LLMs to integrate diverse data sources and perform complex, multi-step reasoning for target identification and drug discovery is a new and promising frontier [47].

AI and deep learning have fundamentally altered the landscape of target space prediction, providing a robust quantitative framework for profiling interactions with therapeutic targets. This technical guide has outlined the core methodologies, from data handling and model architectures to experimental protocols and clinical validation. Within the context of natural products research, these technologies offer a powerful means to systematically decode the mechanism of action of complex natural compounds, thereby bridging the gap between traditional natural product chemistry and modern, data-driven drug discovery. As AI models continue to evolve in sophistication and accessibility, their integration into the chemical biology workflow is poised to unlock new target spaces and dramatically accelerate the journey from traditional remedy to validated therapeutic.

Bioorthogonal Chemistry and Chemoenzymatic Strategies for Probing Function and Diversifying Structures

The exploration of natural products in chemical biology and systematics research has been profoundly transformed by the advent of precise chemical tools. Bioorthogonal chemistry and chemoenzymatic strategies represent two complementary approaches that enable researchers to probe biological function and diversify complex molecular structures with unprecedented precision. These methodologies address fundamental challenges in natural product research, including the need to study biomolecules within their native environments without disruption, and to efficiently access complex natural product scaffolds and their analogues for functional studies [50]. Within the broader thesis of natural products in chemical biology, these techniques provide a critical link between structure and function, allowing for the systematic investigation of biological systems and the expansion of chemical diversity beyond what is accessible through biosynthesis alone.

The significance of these approaches is reflected in their recognition within the scientific community; the 2022 Nobel Prize in Chemistry awarded for the development of bioorthogonal chemistry underscores its transformative impact, while the continued evolution of chemoenzymatic synthesis highlights a paradigm shift in how we approach complex molecule construction [50]. This technical guide details the core principles, current methodologies, and practical applications of these tools, providing researchers with a comprehensive resource for their implementation in chemical biology and natural product research.

Bioorthogonal Chemistry: Principles and Applications

Fundamental Concepts and Reaction Types

Bioorthogonal chemistry refers to a class of chemical reactions that can proceed within living systems without interfering with native biochemical processes. These reactions are characterized by their selectivity, fast kinetics under physiological conditions, and formation of stable, non-toxic products [51]. The development of bioorthogonal tools has enabled researchers to observe and manipulate biomolecules in real-time within complex biological environments, a capability crucial for understanding the function of natural products and their cellular targets.

The evolution of bioorthogonal reactions has progressed from initial Staudinger ligations to more sophisticated copper-free click chemistries, with each generation offering improved kinetics and biocompatibility [52]. Key bioorthogonal reactions used in contemporary research include:

• Strain-Promoted Azide-Alkyne Cycloaddition (SPAAC): This copper-free reaction between azides and strained cyclooctynes offers moderate reaction rates (1–60 M⁻¹ s⁻¹) and excellent biocompatibility, making it suitable for live-cell labeling and in vivo applications [52].
• Inverse Electron Demand Diels-Alder (IEDDA): This reaction between tetrazine and trans-cyclooctene (TCO) derivatives represents one of the fastest bioorthogonal couplings known (1–10⁶ M⁻¹ s⁻¹), enabling rapid labeling even at low reagent concentrations [51] [52].
• Metal-Free Click Chemistry: Encompassing various cycloadditions that proceed without cytotoxic metal catalysts, these reactions have expanded the in vivo applicability of bioorthogonal labeling strategies [50].

Table 1: Comparison of Major Bioorthogonal Reaction Types

Reaction Type	Reactant Pairs	Kinetic Rate (M⁻¹ s⁻¹)	Key Advantages	Primary Applications
Staudinger Ligation	Azide + Phosphine	~0.008	First developed bioorthogonal reaction	Historical importance, limited current use
Copper-Catalyzed Azide-Alkyne (CuAAC)	Azide + Terminal Alkyne	10-100 (with catalyst)	High efficiency	Primarily in vitro applications
Strain-Promoted (SPAAC)	Azide + Strained Cyclooctyne	1-60	Copper-free, good biocompatibility	Live-cell imaging, in vivo labeling
Inverse Diels-Alder (IEDDA)	Tetrazine + trans-Cyclooctene	1-10⁶	Fastest kinetics, high specificity	In vivo targeting, real-time tracking

Metabolic Labeling Strategies

The application of bioorthogonal chemistry typically involves a two-step process beginning with metabolic labeling. This approach leverages the cell's own biosynthetic machinery to incorporate bioorthogonal functional groups into target biomolecules, followed by chemoselective ligation with exogenous probes [52].

Key metabolic labeling strategies include:

• Glycan Labeling: N-azidoacetylmannosamine (ManNAz) and other N-azidoacetylated monosaccharides serve as precursors for the biosynthesis of azide-labeled sialic acids on cell surface glycoproteins and glycolipids [52].
• Lipid Labeling: Azide-modified choline analogs such as azidocholine are incorporated into phospholipids during membrane biosynthesis, enabling selective tagging of cellular membranes [52].
• Protein Labeling: Methionine analogs with bioorthogonal handles can be incorporated into proteins through translation, while monosaccharide derivatives tag glycoprotein surfaces [52].

These labeling strategies create chemically addressable handles on specific classes of biomolecules, which can then be selectively targeted with complementary bioorthogonal probes for imaging, isolation, or functional modulation.

Applications in Biological Imaging and Drug Delivery

Bioorthogonal chemistry has enabled significant advances in imaging and targeted therapeutic delivery, particularly in complex disease states such as cancer and neurodegenerative disorders. The high specificity of these reactions allows for precise localization of imaging agents and therapeutic payloads with minimal off-target effects.

In cancer therapeutics, bioorthogonal chemistry facilitates pretargeted radioimmunotherapy, where a tumor-targeting antibody conjugated with a bioorthogonal handle is administered first, followed by a radiotherapeutic agent bearing the complementary functionality. This approach separates targeting from delivery, significantly reducing nonspecific radiation exposure to healthy tissues [51]. Similarly, bioorthogonal prodrug activation strategies enable localized drug release at disease sites, improving therapeutic indices compared to conventional chemotherapy [51].

For neurodegenerative diseases like Alzheimer's, bioorthogonal tools are being explored to target pathological features such as amyloid-β plaques. The blood-brain barrier presents a significant challenge for conventional therapeutics, but bioorthogonal labeling strategies offer potential solutions through targeted delivery systems that can cross this barrier and specifically engage pathological proteins [51].

In infectious disease research, bioorthogonal chemistry enables specific labeling and tracking of pathogens within host systems. This approach provides insights into host-pathogen interactions and offers novel strategies for targeted antimicrobial delivery [51].

Diagram 1: Bioorthogonal chemistry workflow for biological applications. SPAAC: Strain-promoted azide-alkyne cycloaddition; IEDDA: Inverse electron demand Diels-Alder; TCO: trans-cyclooctene; RIT: Radioimmunotherapy.

Chemoenzymatic Strategies: Synthesis and Diversification

Combining Enzymatic and Synthetic Chemistry

Chemoenzymatic approaches represent a powerful fusion of biological and synthetic methodologies for the construction and diversification of complex natural product scaffolds. These strategies leverage the exquisite selectivity and catalytic efficiency of enzymes while employing traditional synthetic chemistry to access non-natural analogues and install functionality beyond the scope of biosynthetic machinery [53]. This hybrid approach is particularly valuable for addressing the supply challenges associated with low-abundance natural products and for generating structural diversity around bioactive cores for structure-activity relationship studies.

The fundamental advantage of chemoenzymatic strategies lies in their ability to combine the best attributes of both worlds: enzymes provide unparalleled regio-, chemo-, and stereoselectivity under mild, environmentally benign conditions, while synthetic chemistry offers virtually unlimited possibilities for structural variation and introduction of non-natural elements [50] [53]. This synergy is especially evident in the synthesis of complex plant natural products, where selective introduction of chiral centers and oxygenation patterns can be challenging using traditional synthetic approaches alone.

Key Enzymatic Transformations in Natural Product Synthesis

Several classes of enzymes have proven particularly valuable in chemoenzymatic synthesis, enabling transformations that are challenging to achieve with conventional synthetic methods.

Pictet-Spenglerases (PSases) such as norcoclaurine synthase (NCS) and strictosidine synthase (STR) catalyze the stereoselective formation of carbon-carbon bonds between amine and carbonyl functionalities to generate tetrahydroisoquinoline and tetrahydro-β-carboline scaffolds, respectively [53]. These enzymatic transformations form the core structures of numerous alkaloid natural products with precise stereocontrol that is difficult to achieve using chemical catalysts alone.

Table 2: Key Enzymes for Chemoenzymatic Natural Product Synthesis

Enzyme Class	Representative Enzymes	Catalyzed Reaction	Natural Product Applications
Pictet-Spenglerases	Norcoclaurine Synthase (NCS), Strictosidine Synthase (STR)	C-C bond formation between amines and carbonyls	Tetrahydroisoquinoline and indole alkaloids
Oxidoreductases	Berberine Bridge Enzyme (BBE), Monoamine Oxidases (MAO-N)	Redox reactions, deracemization	Various alkaloids including tetrahydroprotoberberines
Biocatalytic Oxidations	Toluene Dioxygenase (TDO)	Arene dihydroxylation	Morphinan and Amaryllidaceae alkaloids
Transferases	Catechol-O-Methyltransferases (COMT)	Methyl transfer	Tetrahydroprotoberberines with specific oxygenation patterns

Oxidoreductases play crucial roles in introducing and manipulating functionality in natural product scaffolds. The berberine bridge enzyme (BBE) performs enantioselective C-C bond formation in the biosynthesis of benzylisoquinoline alkaloids, while engineered monoamine oxidase variants (MAO-N) enable deracemization of amine intermediates through kinetic resolution [53]. These enzymes provide access to enantiopure intermediates that would require complex protecting group strategies and asymmetric synthesis using purely chemical methods.

Biocatalytic oxidation systems, particularly those employing whole-cell catalysts expressing toluene dioxygenase (TDO), enable the synthesis of enantiopure cis-dihydrocatechols from simple arene precursors [53]. These chiral synthons serve as versatile building blocks for the synthesis of various alkaloid families, including morphinan and Amaryllidaceae alkaloids, with the enzyme introducing precise stereochemistry that is maintained throughout the synthetic sequence.

Applications in Complex Molecule Synthesis

Chemoenzymatic strategies have been successfully applied to the synthesis of numerous complex natural products, demonstrating their utility in addressing challenging synthetic problems.

In the synthesis of tetrahydroprotoberberine alkaloids, a one-pot triangular cascade combines a transaminase (CvTAm) for aldehyde generation, a Pictet-Spenglerase (TfNCS) for tetrahydroisoquinoline formation, and a chemical Pictet-Spengler reaction with formaldehyde to construct the tetracyclic core structure [53]. This cascade efficiently assembles the complex alkaloid scaffold with high enantioselectivity (>95% ee) and good conversion (56-99%), demonstrating how enzymatic and chemical steps can be seamlessly integrated in a single reaction vessel.

The synthesis of morphine and related alkaloids has been achieved through a chemoenzymatic approach beginning with TDO-catalyzed dihydroxylation of substituted benzenes to provide enantiopure cis-dihydrocatechols [53]. These chiral building blocks, inaccessible through conventional synthesis with comparable efficiency and selectivity, are then elaborated through chemical steps to construct the complex pentacyclic morphinan scaffold. This approach highlights how enzymatic transformations can provide strategic entry points to complex natural product families.

Plant natural product analogues can be efficiently generated through chemoenzymatic approaches that combine biosynthetic machinery with synthetic diversification. For example, the combination of strictosidine synthase with chemical lactamization, reduction, and glycoside cleavage enables the production of N-substituted tetrahydroangustine analogues with modified biological activities [53]. This strategy creates branch points for analogue generation that would be challenging to access through either purely biological or purely synthetic approaches alone.

Diagram 2: General chemoenzymatic synthesis workflow. Enzymatic transformations provide selective key steps, while chemical synthesis enables diversification and elaboration.

Experimental Protocols and Methodologies

Bioorthogonal Labeling of Extracellular Vesicles

Extracellular vesicles (EVs) play crucial roles in intercellular communication and tissue homeostasis, and their specific labeling and tracking represent an important application of bioorthogonal chemistry [54]. The following protocol describes a method for labeling EV surface components using bioorthogonal chemistry:

Materials:

• Azide-modified monosaccharide (e.g., Ac4ManNAz)
• Dibenzocyclooctyne (DBCO)-conjugated fluorophore
• Phosphate-buffered saline (PBS)
• Ultracentrifuge
• Size-exclusion chromatography columns
• Cell culture medium and reagents

Procedure:

• Metabolic Labeling: Incubate source cells with 50-100 μM Ac4ManNAz in culture medium for 48-72 hours to incorporate azide groups into EV surface glycans.
• EV Isolation: Collect conditioned medium and isolate EVs by differential ultracentrifugation (10,000 × g for 30 minutes to remove debris, followed by 100,000 × g for 70 minutes to pellet EVs) or size-exclusion chromatography.
• Bioorthogonal Labeling: Resuspend EV pellet in PBS containing 10-50 μM DBCO-fluorophore conjugate. Incubate for 1-2 hours at room temperature with gentle agitation.
• Purification: Remove unreacted DBCO-fluorophore by size-exclusion chromatography or additional ultracentrifugation steps.
• Validation: Confirm labeling efficiency and EV integrity using nanoparticle tracking analysis, electron microscopy, and Western blotting for EV markers.

This approach enables specific labeling of EVs without disturbing their biochemical properties and functions, allowing for subsequent tracking of their biodistribution and cellular uptake [54].

Chemoenzymatic Synthesis of Tetrahydroisoquinoline Alkaloids

The following protocol describes a one-pot chemoenzymatic cascade for the synthesis of tetrahydroisoquinoline alkaloids, demonstrating the integration of multiple enzymatic and chemical steps [53]:

Materials:

• Dopamine hydrochloride
• Aldehyde precursor (e.g., 4-hydroxyphenylacetaldehyde)
• Transaminase from Chromobacterium violaceum (CvTAm)
• Thalictrum flavum norcoclaurine synthase (TfNCS)
• Potassium phosphate buffer (100 mM, pH 7.5)
• Pyridoxal phosphate (PLP)
• Formaldehyde solution (37%)

Procedure:

• Reaction Setup: In a reaction vessel, combine dopamine hydrochloride (0.5 mmol), aldehyde precursor (0.55 mmol), CvTAm (5 mg), PLP (0.1 mM), and TfNCS (5 mg) in potassium phosphate buffer (10 mL).
• Transamination and Pictet-Spengler Reaction: Incubate the reaction mixture at 30°C with shaking (200 rpm) for 6-12 hours to allow in situ aldehyde generation and subsequent stereoselective Pictet-Spengler reaction to form (S)-norlaudanosoline.
• Tetrahydroprotoberberine Formation: Add formaldehyde (1.5 mmol) to the reaction mixture and continue incubation for an additional 6 hours to facilitate the chemical Pictet-Spengler reaction forming the tetracyclic tetrahydroprotoberberine scaffold.
• Product Isolation: Extract the reaction mixture with ethyl acetate (3 × 15 mL), combine organic layers, dry over anhydrous sodium sulfate, and concentrate under reduced pressure.
• Purification: Purify the crude product by flash chromatography (silica gel, dichloromethane/methanol gradient) to obtain the tetrahydroprotoberberine alkaloid.

This one-pot cascade achieves the formation of multiple carbon-carbon bonds with high stereoselectivity (>95% ee) and reasonable isolated yields (42%), demonstrating the efficiency of combining enzymatic and chemical transformations [53].

In Vivo Bioorthogonal Prodrug Activation

This protocol outlines a general strategy for in vivo bioorthogonal prodrug activation, highlighting the application of bioorthogonal chemistry for targeted therapeutic delivery [51] [52]:

Materials:

• Prodrug bearing a bioorthogonal masking group (e.g., tetrazine-caged drug)
• trans-Cyclooctene (TCO)-modified targeting antibody
• Sterile saline for injection
• Animal model of disease

Procedure:

• Pretargeting: Administer TCO-modified targeting antibody (5-10 mg/kg) intravenously to allow accumulation at the target site (e.g., tumor tissue). Wait 24-72 hours for clearance from circulation and non-target tissues.
• Prodrug Administration: Inject tetrazine-caged prodrug (dose based on drug potency) intravenously to allow reaction with pretargeted TCO groups at the target site.
• Activation and Release: The IEDDA reaction between tetrazine and TCO occurs rapidly at the target site, releasing the active drug through a self-immolative mechanism.
• Monitoring: Track drug activation and therapeutic response using appropriate imaging modalities or biomarkers.

This pretargeting approach minimizes systemic exposure to active drug and improves the therapeutic index by localizing drug activation specifically to disease sites [52].

Research Reagent Solutions Toolkit

Table 3: Essential Research Reagents for Bioorthogonal and Chemoenzymatic Applications

Reagent Category	Specific Examples	Function/Application	Key Features
Bioorthogonal Handles	Ac4ManNAz, DBCO-sulfo-Cy5, Methyltetrazine-PEG4	Metabolic labeling and detection	Cell permeability, fast kinetics, minimal toxicity
Enzyme Catalysts	TfNCS, STR1, CvTAm, MAO-N variants	Selective bond formation in synthesis	High stereoselectivity, broad substrate tolerance
Chemical Activators	Formaldehyde, acetaldehyde, various benzaldehydes	Scaffold diversification in synthesis	Compatibility with enzyme stability and activity
Analytical Tools	HPLC-HRMS, NMR spectroscopy, molecular networking	Structural characterization and validation	High sensitivity for complex mixture analysis
Biological Systems	Engineered yeast strains, cell lines, animal models	Testing biological activity and distribution	Relevance to human physiology and disease
1H-Phenalene-1,3(2H)-dione	1H-Phenalene-1,3(2H)-dione, CAS:5821-59-0, MF:C13H8O2, MW:196.2 g/mol	Chemical Reagent	Bench Chemicals
Thiodigalactoside	Thiodigalactoside, CAS:51555-87-4, MF:C12H22O10S, MW:358.36 g/mol	Chemical Reagent	Bench Chemicals

Bioorthogonal chemistry and chemoenzymatic strategies have emerged as indispensable tools in the chemical biology of natural products, enabling researchers to bridge the gap between structural complexity and biological function. These approaches provide powerful means to probe biological systems with minimal perturbation and to access complex molecular architectures with unprecedented efficiency and selectivity. As these methodologies continue to evolve, they promise to further accelerate the discovery and development of natural product-inspired therapeutic agents and deepen our understanding of biological systems at the molecular level.

The integration of these chemical tools with emerging technologies in synthetic biology, genomics, and computational chemistry represents the next frontier in natural product research. As noted in recent literature, the field is increasingly moving toward "bioinspired and bio-integrated strategies" that leverage the unique capabilities of both biological and synthetic systems [50]. This convergence approach will likely define the future trajectory of chemical biology, enabling increasingly sophisticated interrogation and manipulation of biological systems for fundamental discovery and therapeutic innovation.

Navigating the Discovery Pipeline: Overcoming Obstacles from Gene to Product

In the fields of chemical biology and systematics, a profound gap exists between the genetic blueprint of an organism and its observable chemical profile, or chemotype. Microbial natural products, traditionally the foundation of many therapeutic agents, are encoded by biosynthetic gene clusters (BGCs). Genomic sequencing has revealed a staggering reality: in prolific producers like Streptomyces, a single genome may encode 25–50 BGCs, yet approximately 90% are silent or cryptic under standard laboratory conditions [55] [56]. These "silent" BGCs are not expressed or are expressed at undetectably low levels, meaning their associated small molecules—which have traditionally served as crucial sources of pharmaceutical inspiration—remain hidden [57] [1]. This discrepancy represents a significant genotype-chemotype gap, leaving an immense reservoir of potential bioactive compounds inaccessible [58].

Bridging this gap is synonymous with understanding the complex dynamics that link genetic information to phenotypic expression, a core goal of physiology and genetics [58]. Unlocking these silent BGCs is therefore not merely a technical challenge but a fundamental scientific pursuit. It promises to dramatically expand our repository of potentially therapeutic small molecules, offering lessons in biosynthesis, chemical ecology, and the physiological roles these compounds play in producing organisms [57] [59]. This guide synthesizes current strategies for activating silent BGCs, providing a technical roadmap for researchers aiming to uncover nature's hidden chemical treasury.

Theoretical Foundations: From Genotype to Chemotype

The relationship between genotype and phenotype can be conceptualized as a Genotype-Phenotype map (GP map), an abstraction of the outcome of highly complex dynamics that include environmental effects [58]. In this context, the chemotype—the portfolio of small molecules an organism produces—is a critical component of the phenome. It is crucial to understand that DNA does not hold a privileged causal position; rather, the system state (phenome) dictates the use of DNA as an inert component, leading to the production of RNAs and proteins that subsequently perturb the system's dynamics. Genetic variations that alter this perturbation regime can lead to different system dynamics and, consequently, to physiological and chemical variation [58].

Computational approaches increasingly utilize a pathway-centric perspective to bridge the genotype-phenotype gap. Causally cohesive Genotype-Phenotype (cGP) models represent a powerful approach where low-level model parameters are explicitly linked to an individual's genotype, and higher-level phenotypes (like the production of a specific metabolite) emerge from mathematical models describing the causal dynamic relationships between these lower-level processes [58]. Furthermore, phenotypic modules—clusters of genes or pathways significantly enriched with genes whose expression changes correlate with phenotypic changes—can be identified by overlaying molecular data onto interaction networks. These modules help explain how organismal-level phenotypes, including the production of specific natural products, arise from coordinated molecular activity [60].

Strategic Approaches to Silent BGC Activation

Activation strategies can be broadly divided into two categories: endogenous approaches, which utilize the native host, and exogenous approaches, which employ a heterologous host for expression [56]. Each paradigm offers distinct advantages and challenges, as detailed in Table 1.

Table 1: Comparison of Endogenous vs. Exogenous Activation Strategies

Feature	Endogenous Activation (Native Host)	Exogenous Activation (Heterologous Host)
Rationale	Leverage native regulatory & biosynthetic machinery	Refactor BGC in a tractable, minimized background
Key Advantage	Physiological relevance; studies of chemical ecology	Bypasses host-specific limitations & complex regulation
Primary Limitation	Native host may be genetically intractable or uncultivable	Biosynthetic requirements may not be met in new host
Best Suited For	Clusters in genetically tractable, well-characterized hosts	Clusters from uncultivable, slow-growing, or intractable organisms

Endogenous Strategies: Awakening Clusters in the Native Host

A. Classical and Reverse Genetics

Classical genetics involves direct manipulation of the native host's genome to induce expression.

• Promoter Engineering: A widely used method involves replacing the native promoter of a silent BGC with a strong, constitutive, or inducible promoter [57] [55]. The advent of CRISPR-Cas9 has revolutionized this approach, enabling precise promoter knock-ins with increased efficiency even in genetically recalcitrant streptomycetes [57]. For instance, this method has been successfully used to activate pigment production in Streptomyces albus and S. lividans, and to induce the production of known compounds like alteramide A and FR-900098, as well as novel metabolites in various Streptomyces strains [57].
• Reporter-Guided Mutant Selection (RGMS): This forward-genetics approach involves generating random mutant libraries (via UV or transposon mutagenesis) and selecting for activation using a reporter system. A reporter gene (e.g., for fluorescence or antibiotic resistance) is fused to the promoter of the target BGC. Mutants are then screened for reporter activity, identifying strains where the silent BGC has been activated [55] [56]. This method has led to the discovery of novel glycosylated gaudimycin analogs and thailandenes, antimicrobial polyenes from Burkholderia thailandensis [56].
• Regulatory Gene Manipulation: Silent BGCs are often controlled by pathway-specific regulators or global regulatory networks. Knocking out transcriptional repressors or overexpressing transcriptional activators can effectively awaken silent clusters. For example, inactivation of a transcriptional repressor via CRISPR-Cas9 activated the scl BGC [55].

B. Chemical Genetics

This genetics-independent approach uses small molecules to elicit BGC expression.

• High-Throughput Elicitor Screening (HiTES): This chemogenetic method involves inserting a reporter gene into a silent BGC to provide a rapid expression readout. The engineered strain is then screened against libraries of small molecules to identify "elicitors" that induce cluster expression [57] [56]. In a notable application, screening a ~500-member natural product library against S. albus identified the pharmaceuticals ivermectin and etoposide as potent inducers of the silent sur BGC, leading to the discovery of 14 novel cryptic metabolites, including the surugamides and albucyclones [57].
• Ribosome Engineering: This approach involves exposing bacteria to sub-inhibitory concentrations of antibiotics that target the ribosome. These treatments can induce phenotypic changes, including the activation of silent BGCs, potentially by perturbing global cellular physiology and stress responses [55].
• Co-culture: Cultivating the target organism alongside another microbe or a consortium simulates natural ecological competition. The biological interactions can trigger defensive chemical responses, activating BGCs that are silent in axenic culture [57].

Exogenous Strategies: Heterologous Expression

Heterologous expression involves cloning the entire silent BGC and transferring it into a genetically tractable surrogate host, thereby removing it from its native regulatory context [55] [56].

• Cloning Large BGCs: Several advanced methods exist for capturing large DNA fragments:
  • Transformation-Associated Recombination (TAR): A yeast-based method that uses homologous recombination to directly clone large BGCs from genomic DNA [55].
  • CRISPR-Cas9 Assisted Cloning: Techniques like CATCH (Cas9-Assisted Targeting of CHromosome segments) use CRISPR-Cas9 to excise specific BGCs from genomic DNA for subsequent cloning [55].
  • Bacteriophage Integrase Systems: Systems utilizing the ΦBT1 attP-attB-int mechanism enable site-specific recombination for cloning BGCs into shuttle vectors [55].
  • ExoCET: A method that combines T4 polymerase-mediated in vitro annealing with homologous recombination to clone very large clusters, such as the 106 kb salinomycin BGC [55].
• Chassis and BGC Engineering: The choice of heterologous host is critical. Common chassis strains like S. albus or S. coelicolor M1146 are engineered to be "minimized" (lacking endogenous BGCs) and "optimized" to provide ample biosynthetic precursors [55]. The BGC itself often requires refactoring—a process that can involve codon optimization, removal of native regulatory elements, and the assembly of synthetic operons under the control of strong, constitutive promoters to ensure robust expression in the new host [55].

Essential Experimental Protocols

Protocol 1: CRISPR-Cas9-Mediated Promoter Replacement

This protocol enables the precise insertion of a constitutive promoter upstream of a target silent BGC.

• Design and Synthesis: Design a CRISPR-Cas9 plasmid containing a guide RNA (gRNA) sequence targeting the desired insertion site immediately upstream of the BGC's first gene. Synthesize a linear DNA donor fragment containing your chosen strong promoter (e.g., ermEp).
• Transformation: Co-transform the CRISPR-Cas9 plasmid and the linear donor DNA fragment into the native host.
• Selection and Screening: Allow homologous recombination to occur. Select for transformants using the plasmid's antibiotic marker. Screen colonies via colony PCR to confirm correct promoter insertion.
• Metabolite Analysis: Ferment the positive clones and analyze the metabolic profile using Liquid Chromatography-Mass Spectrometry (LC-MS) to detect newly produced compounds.

Protocol 2: High-Throughput Elicitor Screening (HiTES)

This protocol uses a reporter system to identify small molecule inducers of silent BGCs.

• Reporter Strain Construction: Fuse a promoterless reporter gene (e.g., gfp for fluorescence, or a resistance marker) to the native promoter of the target silent BGC. Integrate this construct into a neutral site of the native host's chromosome, creating the reporter strain.
• Library Screening: Grow the reporter strain in a 96-well format, adding a different compound from a small-molecule library to each well.
• Detection: After incubation, measure reporter signal (e.g., fluorescence) in each well. Identify "hits"—wells showing significantly elevated signal compared to negative controls.
• Validation and Characterization: Re-treat the wild-type (non-reporter) strain with the hit compounds. Use LC-MS and comparative metabolomics to identify and characterize the novel metabolites produced by the activated BGC.

Visualization of Core Workflows

The following diagrams illustrate the logical relationships and workflows for the primary activation strategies.

The Scientist's Toolkit: Key Research Reagents and Solutions

Successful activation and characterization of silent BGCs rely on a suite of specialized reagents and tools.

Table 2: Essential Research Reagents for Silent BGC Activation

Reagent / Tool	Function / Application	Key Characteristics & Examples
CRISPR-Cas9 Systems	Precise genome editing for promoter replacements, gene knockouts, and cloning.	Enables efficient genetic manipulation in intractable hosts like Streptomyces [57].
Reporter Genes (eGFP, xylE, neo)	Visualizing and selecting for BGC activation in HiTES and RGMS.	eGFP allows fluorescence-based screening; neo (kanamycin resistance) enables selection [57] [56].
TAR Cloning System	Direct cloning of large BGCs (≥50 kb) from genomic DNA.	Yeast-based system using homologous recombination; used with pCAP01 vector [55].
Heterologous Chassis Strains	Surrogate hosts for expressing refactored BGCs.	Minimized strains like S. albus or S. coelicolor M1146 reduce background interference [55].
Bioinformatics Platforms (antiSMASH, PRISM)	In silico identification and prediction of BGCs from genome sequences.	Foundation of genome mining; predicts cluster type, boundary, and potential product [56] [1].
LC-HRMS/MS with Metabolomics	Detecting, quantifying, and structurally characterizing novel metabolites.	Essential for comparing metabolic profiles of engineered vs. wild-type strains [61].
3-Decyl-5,5'-diphenyl-2-thioxo-4-imidazolidinone	3-Decyl-5,5'-diphenyl-2-thioxo-4-imidazolidinone, CAS:875014-22-5, MF:C25H32N2OS, MW:408.6 g/mol	Chemical Reagent
alpha-(Methoxyimino)furan-2-acetic acid	alpha-(Methoxyimino)furan-2-acetic acid, CAS:65866-86-6, MF:C₇H₇NO₄, MW:169.13 g/mol	Chemical Reagent

The systematic activation of silent biosynthetic gene clusters stands as a cornerstone for the future of natural product discovery in chemical biology and systematics. The strategies outlined—from targeted genetic interventions and chemical elicitation to heterologous refactoring—provide a robust, multi-faceted toolkit for bridging the genotype-chemotype gap. The field is moving toward increasingly systematic and comprehensive analyses, as exemplified by pangenomic studies of bacterial genera like Xenorhabdus and Photorhabdus, which map the entirety of their BGC repertoire to identify conserved and unique clusters of ecological importance [59].

Future progress will be driven by the deeper integration of computational models, including causally cohesive genotype-phenotype models that can predict the metabolic outcomes of genetic perturbations [58], and predictive metabolomics that can efficiently link chemical patterns to genetic backgrounds [61]. Furthermore, the application of artificial intelligence and machine learning in genome mining and compound prioritization is poised to dramatically accelerate the discovery process [1]. As these technologies mature, they will not only revitalize natural products as a sustainable source for drug discovery but also profoundly deepen our understanding of the chemical systematics and ecological functions of specialized metabolism across the tree of life.

Natural products and their derivatives represent a cornerstone of modern therapeutics, accounting for over half of all new chemical entities approved by the FDA from 1981 to 2006 [62]. These chemically complex compounds, produced by plants, bacteria, and fungi, have evolved to exhibit profound biological activities that make them invaluable for drug discovery and development. However, their structural complexity, characterized by multiple chiral centers and labile connectivities, presents a fundamental supply problem that hampers research and development efforts. Many natural products are difficult to synthesize chemically and are often produced in minuscule quantities by their native hosts, which can be challenging to culture under laboratory conditions [63] [62].

This whitepaper examines three transformative approaches that are revolutionizing how we address the natural product supply challenge: cell-free synthetic biology, biomimetic synthesis, and advanced metabolic engineering. These methodologies are converging to create a new paradigm in which the sustainable production of complex natural products becomes increasingly feasible, thereby accelerating their investigation within chemical biology and systematics research. By leveraging insights from biosynthesis while incorporating innovative engineering principles, researchers are developing powerful solutions that overcome traditional limitations in natural product sourcing, modification, and scale-up.

Cell-Free Synthetic Biology: A Modular Platform for Natural Product Biosynthesis

Cell-free synthetic biology has emerged as a powerful alternative to whole-cell systems for natural product biosynthesis. This approach utilizes transcriptionally and translationally active cell extracts, devoid of cell walls and membranes, to create modular bioreactor platforms for biomolecular synthesis [63]. The historical foundations of cell-free expression (CFE) systems trace back to Eduard Buchner's pioneering work in the late 19th century, which demonstrated that yeast cell extracts could ferment glucose, and to Marshall Nirenberg's groundbreaking experiments in the 1960s that deciphered the genetic code using E. coli cell-free extracts [63].

Core Principles and Advantages

Cell-free systems function as quasi-chemical bioreactors that can be precisely controlled to produce RNA, peptides, proteins, and small molecules [63]. Unlike whole-cell systems, CFE reactions can be conducted within hours rather than days or weeks, enabling rapid cycling between experimental design and analysis [63]. The open nature of these systems allows researchers to determine and control starting concentrations of substrates and proteins, add purified enzymes and chemicals, and work with linear DNA templates without the need for cloning [63]. Additional advantages include:

• Enhanced Safety: As non-living systems, they require minimal biocostainment [63]
• Stability: Freeze-dried extracts can be transported at room temperature and reactivated upon rehydration [63]
• Scalability: Reactions can be scaled from microfluidic devices to 100L volumes with demonstrated linearity and low variability [63]
• Extended Operation: Reaction duration can be prolonged to 30 hours through dialysis or microfluidics that replenish substrates and remove metabolic byproducts [63]

Implementation for Natural Products

Cell-free technologies have been successfully applied to diverse classes of natural products, including ribosomal peptides, polyketides (PKs), and nonribosomal peptides (NRPs) [63] [64]. For these complex compounds, two primary cell-free approaches have been developed:

• Purified Enzyme Systems: Reconstitute entire biosynthetic pathways using individually purified enzymes [64]
• Cell-Free Protein Synthesis (CFPS): Combine protein production and natural product biosynthesis in a single system [64]

Table 1: Cell-Free Systems for Major Natural Product Classes

Natural Product Class	Key Enzymatic Machinery	Cell-Free Applications	Notable Achievements
Ribosomal Peptides (RiPPs)	Radical SAM enzymes, precursor peptides	Antimicrobial discovery, pathway prototyping	Engineering of aromatic crosslinking enzymes [63] [65]
Nonribosomal Peptides (NRPs)	Nonribosomal peptide synthetases (NRPSs)	In vitro biosynthesis, analog generation	Activation of "cryptic" biosynthetic pathways [63]
Polyketides (PKs)	Polyketide synthases (PKSs)	Pathway characterization, novel compound production	Heterologous expression of modular PKS in E. coli extracts [63] [62]
Terpenoids	Terpene synthases, cytochrome P450s	Rapid prototyping of biosynthetic pathways	Reconstruction of complex oxidation cascades [66]

Experimental Protocol: Cell-Free Biosynthesis of Ribosomal Peptides

Objective: Produce and modify a ribosomal peptide natural product using a cell-free system.

Materials:

• E. coli or Streptomyces cell-free extract [63]
• DNA template encoding precursor peptide and modifying enzymes [63]
• Energy solution (ATP, GTP, CTP, UTP) [63]
• Amino acid mixture (all 20 canonical amino acids)
• Mg²⁺, K⁺, and other essential cofactors
• Post-translational modification cofactors (as required)

Procedure:

• Reaction Setup: Combine 12 μL cell-free extract, 2 μL energy solution, 2 μL amino acid mixture, 1 μL DNA template (25 ng/μL), and 3 μL cofactor solution [63]
• Incubation: Maintain reaction at 30°C for 4-16 hours with gentle agitation
• Monitoring: Analyze peptide formation via LC-MS/MS at 2-hour intervals
• Modification: Add specific cofactors (e.g., S-adenosylmethionine for methylation) to enable post-translational modifications
• Product Isolation: Terminate reaction by heating to 75°C for 10 minutes, remove precipitates by centrifugation, and purify peptide using solid-phase extraction

Applications: This protocol enables rapid prototyping of RiPP biosynthetic pathways, exploration of enzyme specificity, and production of novel analogs through substrate promiscuity [63].

Metabolic Engineering Strategies for Enhanced Natural Product Production

Metabolic engineering applies rational genetic modifications to optimize an organism's metabolic profile and biosynthetic capabilities [62]. For natural products, this approach primarily focuses on two objectives: increasing target compound titers and modifying natural product scaffolds to improve pharmacological properties [62].

Strain Improvement in Native Producers

Traditional strain improvement relied on random mutation and selection, exemplified by the development of industrial Penicillium chrysogenum strains that produce penicillin at approximately 100,000-fold higher titers than Fleming's original isolate [62]. Modern metabolic engineering employs more targeted strategies:

• Precursor Enhancement: Overexpression of genes encoding rate-limiting enzymes in precursor supply pathways
• Bottleneck Relief: Identification and overexpression of rate-limiting biosynthetic enzymes
• Competitive Pathway Reduction: Knockout of genes directing flux toward unwanted byproducts
• Regulatory Manipulation: Modification of transcriptional regulators that control biosynthetic gene clusters (BGCs)

Heterologous Production Platforms

Many native producers grow slowly, are genetically intractable, or produce complex mixtures of secondary metabolites, making heterologous hosts an attractive alternative [62]. The selection of an appropriate heterologous host depends on the source of the pathway and the type of metabolite:

Table 2: Comparison of Heterologous Hosts for Natural Product Production

Host Organism	Advantages	Limitations	Successful Applications
E. coli	Fast growth, well-established genetics, easy manipulation	May lack necessary precursors or modification machinery	Erythromycin, complex polyketides, nonribosomal peptides [62]
Streptomyces spp.	Native ability to produce antibiotics, possesses necessary precursors	Slower growth, more complex genetics, produces competing metabolites	Daptomycin, tetracenomycin [62]
S. cerevisiae	Eukaryotic protein processing, generally recognized as safe (GRAS) status	Limited precursor supply for some bacterial natural products	Plant-derived terpenoids, alkaloids [62]

Computational Pathway Design with SubNetX

Recent advances in computational tools have dramatically enhanced our ability to design optimized biosynthetic pathways. The SubNetX algorithm represents a particularly powerful approach that combines constraint-based and retrobiosynthesis methods to identify balanced biosynthetic subnetworks [67].

Methodology:

• Reaction Network Preparation: Compile a database of elementally balanced reactions
• Graph Search: Identify linear core pathways from precursor to target compounds
• Subnetwork Expansion: Connect cosubstrates and byproducts to native metabolism
• Host Integration: Incorporate subnetwork into genome-scale metabolic model of host
• Pathway Ranking: Evaluate feasible pathways based on yield, enzyme specificity, and thermodynamic feasibility [67]

Application: SubNetX has been successfully applied to 70 industrially relevant natural and synthetic chemicals, demonstrating the ability to identify viable pathways with higher production yields compared to linear pathways [67]. For example, the algorithm successfully designed a balanced pathway for scopolamine production in E. coli by supplementing gaps in the ARBRE biochemical network with reactions from the ATLASx database [67].

Experimental Protocol: Metabolic Engineering ofE. colifor Polyketide Production

Objective: Engineer E. coli to produce 6-deoxyerythronolide B (6dEB), the macrocyclic core of erythromycin.

Genetic Modifications:

• Insert PPTase Gene: Integrate the sfp phosphopantetheine transferase gene from Bacillus subtilis into the chromosome to enable post-translational activation of polyketide synthases [62]
• Enhance Extender Unit Supply: Introduce pccA and pccB genes from Streptomyces coelicolor to convert propionyl-CoA to (2S)-methylmalonyl-CoA [62]
• Optimize Precursor Availability: Delete the prpPBCD operon to eliminate propionate catabolism and overexpress prpE to enhance propionyl-CoA ligase activity [62]
• Express Biosynthetic Genes: Introduce the three DEBS genes (DEBS1, DEBS2, DEBS3) encoding the 6dEB synthase on compatible expression vectors [62]

Fermentation Conditions:

• Medium: M9 minimal medium supplemented with 0.5% glycerol, 10 mM propionate
• Induction: Add 0.5 mM IPTG at OD600 = 0.6
• Harvest: 48 hours post-induction
• Analysis: Extract with equal volume of ethyl acetate and analyze by LC-MS

Expected Outcome: Engineered strains typically produce 6dEB at approximately 0.1 mmol per gram of cellular protein per day [62].

Biomimetic Synthesis: Bridging Chemical and Biological Paradigms

Biomimetic synthesis strategies draw inspiration from biosynthetic pathways to develop more efficient chemical syntheses of complex natural products. This approach combines the precision of organic synthesis with the efficiency evolved in biological systems.

Key Strategies and Recent Advances

Chemoenzymatic Synthesis: This hybrid approach combines chemical synthesis with enzymatic transformations, leveraging the efficiency and selectivity of biosynthetic enzymes for challenging transformations. A prominent example is the total synthesis of alchivemycin A, which employed de novo skeleton construction followed by a late-stage enzymatic oxidation cascade using engineered enzymes [66].

Radical Retrosynthesis: Inspired by biosynthetic radical mechanisms, this approach has enabled concise syntheses of complex molecules. For instance, a recent synthesis of saxitoxin and its derivatives utilized radical reactions in combination with biocatalysis and C-H functionalization to achieve the synthesis in fewer than ten steps [66].

Enantioselective Hydrogenation: Asymmetric hydrogenation strategies have streamlined the synthesis of chiral natural products. Recent work has demonstrated that hydrogenation of tetrasubstituted 1,2-dihydronaphthalene esters provides efficient access to more than 30 cyclolignan natural products [66].

Experimental Protocol: Chemoenzymatic Synthesis of Alchivemycin A

Objective: Complete the total synthesis of alchivemycin A using a chemoenzymatic approach.

Chemical Synthesis Steps:

• Core Construction: Synthesize the polyketide core structure using iterative aldol reactions and functional group manipulations
• Functionalization: Introduce necessary oxygenated functionalities through selective oxidation and protection/deprotection sequences

Enzymatic Transformation:

• Enzyme Engineering: Rationality engineer the key oxidase enzyme (AlcO) to enhance activity and stability through site-directed mutagenesis [66]
• Oxidation Cascade: Incubate the synthetic core structure (5 mM) with purified AlcO (0.1 mg/mL) in potassium phosphate buffer (50 mM, pH 7.5) with NADPH (2 mM) at 30°C for 12 hours
• Product Isolation: Extract with ethyl acetate, concentrate under reduced pressure, and purify by preparative HPLC

Yield Optimization: Through protein engineering, the final oxidation step can achieve yields exceeding 80%, significantly improving overall synthetic efficiency [66].

Table 3: Key Research Reagents for Natural Product Supply Solutions

Reagent/Resource	Function	Application Examples	Key Characteristics
Cell-Free Extracts	Provide transcriptional/translational machinery	RiPP production, pathway prototyping	E. coli, Streptomyces, or wheat germ sources; lyophilization compatible [63]
Phosphopantetheine Transferases	Activate carrier proteins in PKS/NRPS systems	Heterologous expression of polyketides and nonribosomal peptides	Sfp from B. subtilis; broad substrate specificity [62]
Bioinformatic Tools (antiSMASH, SubNetX)	Identify and design biosynthetic pathways	Genome mining, pathway prediction	Algorithmic pathway ranking based on yield and feasibility [63] [67]
Balanced Cofactor Systems	Maintain redox and energy balance	In vitro reconstructions, cell-free systems	NADPH/NADP⁺, ATP/ADP regeneration systems [63]
Chassis Strains	Optimized heterologous production hosts	Metabolic engineering, heterologous expression	E. coli BAP1, S. coelicolor CH999, S. lividans K4-114 [62]

The supply problem for complex natural products represents a significant bottleneck in chemical biology and drug discovery research. However, the convergence of cell-free systems, biomimetic synthesis, and advanced metabolic engineering is creating a powerful toolkit to address this challenge. Cell-free synthetic biology offers unprecedented modularity and control for pathway prototyping and natural product production. Metabolic engineering, enhanced by computational tools like SubNetX, enables the optimization of complex biosynthetic pathways in both native and heterologous hosts. Biomimetic synthesis strategies bridge chemical and biological approaches, leveraging nature's efficiency while enabling synthetic diversification.

These approaches are not mutually exclusive but rather complementary technologies that can be integrated to create comprehensive solutions for natural product supply. As these methodologies continue to advance, they will undoubtedly accelerate the discovery, development, and production of natural product-based therapeutics, supporting their continued importance in chemical biology and systems research. The ongoing refinement of these technologies promises to unlock the vast potential of nature's chemical diversity for biomedical applications.

Natural Products (NPs) are granted a privileged status in drug discovery, with nearly half of all new FDA-approved drugs being NPs or their derivatives [68]. However, Complex Natural Products (CNPs), characterized by polycyclic structures, abundant stereochemistry, and nonrepetitive structural units, present a significant analytical challenge [68]. Unlike simpler lipids or peptides, the structural annotation of CNPs remains a major bottleneck in their utilization [68]. This technical guide outlines advanced methodologies for deconvoluting complex natural extracts and efficiently identifying lead compounds, framing these techniques within the broader context of chemical biology and systematics research. The goal is to provide researchers with a structured approach to transform complex mixtures into validated, high-quality leads suitable for further development.

Analytical Foundations: Profiling Complex Natural Extracts

The first step in managing complexity is the separation and accurate profiling of the constituents within a natural extract. Advanced analytical techniques are critical for this phase.

Liquid Chromatography-Mass Spectrometry (LC-MS) and Annotation Strategies

Liquid chromatography coupled to mass spectrometry (LC-MS) is a cornerstone technique for rapid, high-throughput screening of natural extracts, capable of measuring thousands of metabolic features from small quantities of material [68]. Tandem mass spectrometry (MS/MS) provides structural information that is key for annotation.

• Conventional Database Matching: This approach matches experimental MS/MS spectra against public or in-silico databases. A significant limitation is that publicly available experimental MS data covers less than 5% of reported NPs, drastically limiting annotation scope [68].
• Molecular Networking: Strategies like Global Natural Products Social Molecular Networking (GNPS) cluster metabolites based on the similarity of their product ion spectra, providing a comprehensive and non-selective overview of the chemical space within a sample [68].
• Modular Fragmentation-Based Structural Assembly (MFSA): This innovative strategy disassembles target CNP structures into modules based on common fragmentation patterns, recognizes them via a pseudo-library, and reassembles the structure using characteristic ions and neutral losses [68]. A user-friendly application, CNPs-MFSA, coded in Python, has been developed to implement this strategy. In a benchmark study targeting daphnane-type diterpenoids, CNPs-MFSA outperformed common tools like SIRIUS, MS-FINDER, and MetFrag in Top-1 annotation accuracy [68].

Orthogonal Analytical Techniques

• Gas Chromatography with Ion Mobility Spectrometry (GC-IMS): This technique provides a powerful two-dimensional separation orthogonal to MS. A recent study evaluated a Fourier deconvolution ion mobility spectrometer (FDIMS) coupled to GC for analyzing volatile compounds in plant extracts [69]. This GC-FDIMS setup demonstrated higher resolving power, lower detection limits, and a wider linear range compared to traditional signal-averaging IMS methods, making it highly suitable for quantifying flavor and fragrance compounds [69].
• Nuclear Magnetic Resonance (NMR) Spectroscopy: While often requiring milligram-scale quantities of pure compounds and being time-consuming, NMR remains a gold standard for distinguishing isomers and determining stereochemistry [68].

Table 1: Summary of Key Analytical Techniques for Profiling Natural Extracts

Technique	Key Principle	Strengths	Common Applications in NP Research
LC-MS/MS	Separation by LC followed by mass analysis and fragmentation.	High sensitivity, high-throughput, provides structural data.	General metabolite profiling, dereplication.
Molecular Networking (e.g., GNPS)	Clustering of MS/MS spectra based on cosine similarity.	Provides an untargeted overview of chemical relationships in a sample.	Discovering new analogs, visualizing chemical diversity.
MFSA (e.g., CNPs-MFSA)	Target annotation via modular dis-/assembly of fragmentation patterns.	High accuracy for specific, complex NP classes; breaks known chemical boundaries.	Targeted annotation of CNP classes like daphnanes, aconitines.
GC-IMS	Gas-phase separation followed by drift-time separation based on size/shape/charge.	Orthogonal separation, high sensitivity for volatiles, atmospheric pressure operation.	Analysis of essential oils, plant volatiles, flavors.
NMR	Explores magnetic properties of atomic nuclei.	Determines planar structure and stereochemistry, distinguishes isomers.	Full structural elucidation of isolated pure compounds.

Computational and In-Silico Approaches

Computational methods are indispensable for navigating the vast chemical spaces of natural products and prioritizing experiments.

Virtual Screening and Hierarchical Workflows

Virtual Screening (VS) serves as a cost-effective method to triage large chemical spaces before wet-lab testing. It uses structure-based docking, ligand-based pharmacophores, or machine learning (ML) models to predict small molecule interactions with a target protein [70]. A significant challenge is the computational cost of screening trillion-scale on-demand chemical collections [71].

A proposed solution is a bottom-up, hierarchical workflow that trades speed for accuracy at each step [71]. This approach involves:

• Exploration Phase: An exhaustive virtual screen of a fragment-sized chemical space (up to 14 heavy atoms) to identify low molecular-weight compounds with high ligand efficiency.
• Exploitation Phase: The growth of these fragment hits into drug-sized compounds by enumerating focused libraries based on the essential binding core.
• Hierarchical Filtering: Applying increasingly sophisticated computational methods to a smaller number of compounds, such as:
  • Molecular Docking: For initial binding pose prediction.
  • MM/GBSA (Molecular Mechanics-Generalized Born Surface Area): To rank molecules by solvation energy.
  • Dynamic Undocking (DUck): An MD-based method to measure the work required to break a key protein-ligand interaction, providing a robust assessment of binding stability [71].

Data Analysis and Clustering for Molecular Screening

Efficient molecular screening requires intelligent clustering and similarity analysis. One innovative framework uses scaffold-driven fuzzy similarity and adaptive spectral clustering [72]. This method uses molecular scaffolds (core structures) to narrow the chemical space and applies fuzzy logic for a more nuanced classification of molecular similarity, enhancing screening efficiency and the identification of homologous compounds [72].

Diagram 1: Bottom-up lead identification workflow.

From Hit to Lead: Experimental Strategies for Lead Identification

Once a natural extract is profiled and computational prioritization is complete, experimental strategies are required to identify and validate lead compounds.

Defining a High-Quality Hit

A hit is a compound that meets specific criteria to be considered a viable starting point for optimization. Key criteria for a high-quality hit include [70]:

• Confirmed Activity: Reproducible activity in a primary assay with a concentration-response (typically micromolar potency).
• Selectivity: Clean profile in counter-screens against related targets; not a pan-assay interference compound (PAINS).
• Tractability: Synthetically accessible with clear points for analog design.
• Early ADME: Acceptable preliminary properties for solubility and stability.

Experimental Hit Identification Methods

Table 2: Key Experimental Methods for Hit Identification

Method	Principle	Typical Library Size	Advantages	Limitations
High-Throughput Screening (HTS)	Automated testing of plated compound libraries in a biochemical or cellular assay.	10⁵ - 10⁶ compounds [70].	Direct functional readout; mature automation.	High cost; assay development burden; false positives [70].
DNA-Encoded Library (DEL) Screening	Affinity selection of DNA-barcoded small molecules; binders identified via PCR/NGS.	10⁶ - 10⁹+ compounds in a single tube [70].	Unprecedented library size; rapid screening.	Requires off-DNA resynthesis; potential for false positives from truncates [70].
Fragment-Based Screening (FBS)	Screening of low molecular weight compounds (<300 Da) followed by structural-guided growth.	10³ - 10⁴ fragments [70].	High ligand efficiency; covers diverse chemical space efficiently.	Requires sensitive biophysical methods (SPR, NMR, X-ray).

Advanced DEL technologies, such as the Binder Trap Enrichment (BTE) and cellular BTE (cBTE) platforms, address traditional limitations by avoiding target immobilization and enabling screening inside living cells, respectively. This expands the target space and increases physiological relevance [70].

The Scientist's Toolkit: Essential Reagents and Materials

Successful deconvolution and lead identification rely on a suite of specialized reagents, libraries, and software.

Table 3: Essential Research Reagent Solutions and Tools

Tool / Reagent	Function / Description	Application in Workflow
Enamine REAL Space	An ultra-large, on-demand chemical library of billions of synthesizable compounds [71].	Source of compounds for virtual screening and scaffold expansion in the exploitation phase.
CNPs-MFSA (Python App)	A user-friendly application for the Modular Fragmentation–based Structural Assembly of Complex Natural Products [68].	Targeted structural annotation of specific CNP classes from LC-MS/MS data.
63Ni Ionization Source	A radioactive source providing stable ionization efficiency in Ion Mobility Spectrometers [69].	Reliable ionization for GC-IMS analysis of volatile compounds.
DELs (DNA-Encoded Libraries)	Combinatorial libraries where each small molecule is covalently linked to a unique DNA barcode.	Ultra-high-throughput affinity-based screening against purified protein or in cellular environments (cBTE).
YoctoReactor	A proprietary technology for synthesizing DELs with high code-to-compound fidelity, minimizing truncated molecules [70].	Production of high-fidelity DELs to reduce false positive rates during hit identification.
SPR (Surface Plasmon Resonance)	A biophysical technique to monitor biomolecular interactions in real-time without labeling.	Label-free confirmation of binding kinetics and affinity for hits from DEL or virtual screens.

Integrated Experimental Protocols

Protocol: MFSA-Based Annotation of Target CNPs from Crude Extract

This protocol uses the CNPs-MFSA strategy for targeted annotation [68].

• Sample Preparation and LC-MS/MS Analysis:

  • Prepare a crude natural extract using standard solvent extraction (e.g., methanol or dichloromethane).
  • Separate the extract using reversed-phase liquid chromatography (e.g., C18 column).
  • Acquire data-dependent MS/MS spectra for ions above a predefined intensity threshold on a high-resolution mass spectrometer.

• Module Definition and Pseudo-Library Construction (Pre-processing):

  • For the target CNP class (e.g., daphnanes), disassemble known core structures into modules based on common fragmentation sites (e.g., unstable C-O bonds).
  • Define modules based on characteristic product ions and neutral losses.
  • Build an in-silico pseudo-library encompassing all currently reported and theoretically possible structures for the target CNP class.

• Data Processing with CNPs-MFSA:

  • Input the collected MS/MS data into the CNPs-MFSA application.
  • The software will: a) Recognize target CNPs by matching characteristic ions against the pseudo-library. b) Annotate module structures by labeling characteristic ions and neutral losses. c) Reassemble the annotated modules to generate candidate structures.

• Validation:

  • Compare the annotated results with existing literature and databases.
  • Where possible, isolate predicted compounds using preparatory-scale LC and confirm structures via NMR.

Protocol: Bottom-Up Identification of BRD4 Inhibitors

This protocol is adapted from a prospective study that identified novel BRD4 (BD1) binders [71].

• Druggability Assessment and Pharmacophore Definition:

  • Perform MDMix simulations on the target protein (e.g., BRD4 BD1) to identify interaction hotspots.
  • Define a pharmacophore model based on these hotspots (e.g., a hydrogen bond donor to Asn140 and a hydrophobic feature).

• Exploration Phase: Virtual Fragment Screening:

  • Dock a virtual fragment library (e.g., ~4 million compounds from ZINC20/Enamine REAL) against the target binding site, using the pharmacophore as a restraint.
  • Group the top-scoring fragments into ~2000 clusters using a chemical signaturizer to maximize diversity.
  • Calculate the binding energy (ΔGbind) for cluster representatives using MM/GBSA and filter out clusters with ΔGbind > -30.0 kcal/mol.
  • Apply Dynamic Undocking (DUck) to the remaining fragments and set a threshold (e.g., WQB > 7.0 kcal/mol) to select the final fragment hits.

• Exploitation Phase: Scaffold Expansion:

  • For each validated fragment hit, use a tool like SpaceMACS to search an ultra-large database (e.g., Enamine REAL Space) for drug-sized compounds containing the fragment's scaffold.
  • Apply drug-like filters (e.g., Lipinski's Rule of Five) to the resulting focused library.
  • Subject the filtered library to the same hierarchical computational filtering as in the exploration phase (Docking -> MM/GBSA -> DUck).

• Experimental Validation:

  • Procure or synthesize the top-ranked compounds.
  • Perform a double single-dose screening using Differential Scanning Fluorimetry (DSF) and Surface Plasmon Resonance (SPR).
  • Seek to confirm the binding mode via X-ray crystallography.
  • Determine quantitative binding affinity using a dose-response assay (e.g., TR-FRET).

Diagram 2: MFSA structural annotation workflow.

Natural products (NPs) are an indispensable source of novel therapeutics, with more than half of all FDA-approved small-molecule drugs originating from natural sources [73]. In the context of chemical biology and systematics research, they provide unique chemical scaffolds optimized by evolution for biological interaction. However, their translation into effective therapies faces three fundamental challenges: poor systemic bioavailability due to unfavorable physicochemical properties, undefined target specificity that obscures mechanisms of action, and compound-specific toxicity that can limit therapeutic windows. This technical guide synthesizes contemporary strategies to address these challenges, providing researchers with a framework for optimizing natural products for functional application in drug discovery and development.

Enhancing the Bioavailability of Natural Products

Bioavailability refers to the proportion and rate at which an active ingredient is released from a formulation, absorbed through the gastrointestinal tract, and becomes available at the site of physiological action [74]. For natural products, low bioavailability is frequently attributed to poor aqueous solubility, limited intestinal permeability, and instability under physiological conditions [75] [76].

Core Challenges in Natural Product Bioavailability

The inherent physicochemical properties of many natural products create significant delivery barriers. Key limiting factors include:

• Poor Water Solubility: Highly hydrophobic compounds like octacosanol exhibit extremely low oral bioavailability, with serum concentrations reaching only nanogram-per-milliliter levels even at high dosing [74].
• First-Pass Metabolism: Extensive hepatic metabolism significantly reduces systemic exposure for many polyphenols and alkaloids before reaching circulation [76].
• Chemical Instability: Many active natural compounds degrade under gastrointestinal pH conditions or during storage, reducing their effective concentration [76].

Technological Strategies for Bioavailability Enhancement

Advanced formulation and delivery strategies can fundamentally overcome these bioavailability limitations.

Table 1: Strategies for Enhancing Natural Product Bioavailability

Strategy	Technology Examples	Mechanism of Action	Representative Applications
Particle Size Reduction	Nanocrystals, Nanoemulsions	Increased surface area for dissolution	Octacosanol nanocrystals showing enhanced absorption [74]
Lipidic Systems	Microemulsions, Liposomes, Solid Lipid Nanoparticles	Improved solubilization and lymphatic uptake	Curcumin proliposomes for lung delivery [76]
Polymer-Based Carriers	Micelles, Solid Dispersions, Microencapsulation	Molecular dispersion and stability enhancement	Soy protein isolate-octacosanol nanocomplex [74]
Alternative Delivery Routes	Dry Powder Inhalers (DPI)	Avoidance of first-pass metabolism	Pulmonary delivery of resveratrol and silymarin [76]

Nanotechnology Approaches: Nano-formulations address multiple limitations simultaneously. For octacosanol, PEG-derivatized micelles have been developed to carry paclitaxel, while nanoemulsions synthesized through green processes significantly improve gastrointestinal absorption [74]. These systems enhance bioaccessibility, protect against degradation, and can facilitate targeted delivery.

Pulmonary Delivery Systems: Dry powder inhalers (DPIs) represent a particularly promising approach for bioavailability enhancement. The lungs offer a large surface area (approximately 100m²), abundant capillaries, and minimal first-pass metabolism, enabling direct access to systemic circulation [76]. Spray drying technology allows precise control of particle size (1-5μm optimal for alveolar deposition), transforms crystalline drugs into more soluble amorphous solid dispersions, and enhances stability through appropriate polymer selection [76].

Experimental Protocol: Spray Drying for Pulmonary Delivery

Objective: Produce stable, inhalable dry powder particles of a natural product with optimized pulmonary deposition characteristics.

Materials:

• Natural product extract (e.g., resveratrol, curcumin, oridonin)
• Carrier/excipient (lactose, mannitol, or biodegradable polymers)
• Spray dryer (e.g., Büchi Mini Spray Dryer B-290)
• Solvent system (typically aqueous, ethanol, or mixed)

Methodology:

• Feed Solution Preparation: Dissolve the natural product and carrier (typically 1:1 to 1:3 ratio) in an appropriate solvent with constant stirring.
• Parameter Optimization: Calibrate the spray dryer with the following key parameters:
  • Inlet temperature: 100-150°C (compound-dependent)
  • Outlet temperature: 40-60°C
  • Aspirator flow rate: 90-100%
  • Feed flow rate: 3-5 mL/min
• Particle Collection: Collect the dried powder from the collection chamber and store in desiccated conditions.
• Characterization:
  • Determine particle size distribution by laser diffraction
  • Assess morphology by scanning electron microscopy (SEM)
  • Analyze solid state by X-ray powder diffraction (XRPD)
  • Evaluate aerosol performance using next-generation impactor (NGI)

Validation: In vitro dissolution rates should show significant improvement over unformulated compound. For resveratrol, spray-dried particles demonstrated equivalent antioxidant activity to vitamin C while achieving optimal particle size for alveolar deposition [76].

Advancing Target Specificity Through Chemical Proteomics

Understanding the protein targets of natural products is fundamental to elucidating their mechanisms of action, optimizing efficacy, and minimizing off-target effects [73]. Target identification has evolved from single-target approaches to comprehensive proteome-wide profiling enabled by chemical proteomics.

Chemical Proteomics Approaches for Target Identification

Chemical proteomics integrates synthetic chemistry, cellular biology, and mass spectrometry to comprehensively identify protein targets of bioactive small molecules [73]. Two primary frameworks dominate the field:

Compound-Centric Chemical Proteomics (CCCP): This approach originates from classical drug affinity chromatography, where natural products are immobilized on solid supports (e.g., magnetic or agarose beads) to serve as bait for capturing target proteins from cell or tissue lysates [73]. The immobilized probes are incubated with biological samples, followed by extensive washing to remove nonspecific binders, then elution and identification of specifically bound proteins.

Activity-Based Protein Profiling (ABPP): ABPP uses activity-based probes that covalently modify the active sites of enzymes or functional protein domains based on their biochemical activity [73]. These probes typically contain a reactive group that binds the target, a linker region, and a tag (e.g., biotin or alkyne) for enrichment or detection.

Table 2: Target Identification Methods for Natural Products

Method Category	Specific Techniques	Key Principles	Applications
Label-Based Methods	Immobilized Probes, ABPP, Click Chemistry	Compound modification with tags/biotin for enrichment	FK506 target identification [73]
Label-Free Methods	Thermal Proteome Profiling (TPP), Drug Affinity Responsive Target Stability (DARTS)	Monitoring protein stability/solubility changes upon ligand binding	Target identification for unmodified natural products [77]
Bioinformatics-Driven	Molecular Docking, Chemoproteomics	Computational prediction combined with experimental validation	Ginsenoside CK target identification [78]

Experimental Protocol: Affinity Purification Using Immobilized Probes

Objective: Identify protein targets of a natural product using affinity purification and mass spectrometry.

Materials:

• Natural product of interest with known structure-activity relationship
• NHS-activated agarose or magnetic beads
• Cell lysate (from relevant tissue or cell lines)
• Centrifugal filters (10-30 kDa MWCO)
• LC-MS/MS system

Methodology:

• Probe Design and Synthesis:
  • Identify appropriate attachment site on natural product that does not interfere with bioactivity through SAR studies
  • Synthesize derivative with appropriate linker (e.g., alkyl chain, PEG spacer) and reactive group (amine, thiol)
  • Immobilize derivative on NHS-activated beads according to manufacturer protocol
  • Prepare control beads with identical chemistry but lacking the natural product

• Target Fishing:

  • Prepare cell lysate in appropriate buffer (e.g., PBS with protease inhibitors)
  • Pre-clear lysate with control beads for 1 hour at 4°C
  • Incubate pre-cleared lysate with natural product-conjugated beads for 2-4 hours at 4°C
  • Wash beads extensively with buffer to remove nonspecific binders
  • Elute bound proteins with Laemmli buffer or competitive elution with free natural product

• Protein Identification:

  • Separate eluted proteins by SDS-PAGE and perform in-gel tryptic digestion
  • Analyze peptides by LC-MS/MS
  • Process MS data using database search algorithms (MaxQuant, Proteome Discoverer)
  • Identify specific binders by comparing to control bead samples

Validation: Confirm identified targets through complementary approaches:

• Surface Plasmon Resonance (SPR) for binding affinity measurements
• Cellular Thermal Shift Assay (CETSA) to monitor target engagement in cells
• Genetic knockdown/knockout to assess phenotypic concordance

Recent applications include identifying peroxiredoxin 6 as a direct target of withangulatin A in non-small cell lung cancer [78] and comprehensive target mapping for artemisinin derivatives [73].

Mitigating Toxicity of Natural Products

Toxicity represents a significant limitation in the development of natural product-based therapeutics. Understanding and mitigating toxicological profiles is essential for successful clinical translation.

Mechanisms of Natural Product Toxicity

Natural products can exert toxicity through several mechanisms:

• Reactive Functional Groups: Compounds with electrophilic moieties can covalently modify cellular macromolecules
• Off-Target Interactions: Binding to unintended biological targets can produce adverse effects
• Metabolic Activation: Biotransformation can generate reactive intermediates that cause cellular damage
• Drug-Drug Interactions: Modulation of drug-metabolizing enzymes (e.g., CYP450) can alter pharmacokinetics of co-administered drugs

Strategic Approaches for Toxicity Reduction

Chelation Therapy Enhancement: For metal-induced toxicity such as arsenic poisoning, natural dietary compounds can enhance detoxification. Arsenic accumulates in the body through chronic exposure, leading to multisystem toxicity including skin lesions, cancer, and organ damage [79]. The mechanism involves arsenic binding to critical cellular targets including pyruvate dehydrogenase (through dihydrolipoic acid coordination), glutathione-related enzymes, and thioredoxin reductase (via selenol group interaction) [79]. Natural compounds including vitamins (A, C, E), polyphenols (green tea), curcumin, and selenium can regulate glutathione and antioxidant enzymes (catalase, superoxide dismutase, glutathione peroxidase), providing protective effects against arsenic toxicity [79].

Structural Modification Strategies:

• Prodrug Design: Masking reactive functionalities until site-specific activation
• Isosteric Replacement: Substituting toxic moieties with bioequivalent groups
• Conformational Constraint: Restricting molecular flexibility to enhance target specificity

Formulation-Based Detoxification: Advanced delivery systems can minimize exposure to sensitive tissues while maintaining therapeutic efficacy at target sites.

Experimental Protocol: Assessing Natural Product-Mediated Toxicity Protection

Objective: Evaluate the protective effects of natural compounds against arsenic-induced toxicity in a cellular model.

Materials:

• HepG2 cells (human hepatoma cell line) or primary hepatocytes
• Sodium arsenite (NaAsOâ‚‚)
• Natural test compounds (e.g., curcumin, resveratrol, epigallocatechin gallate)
• MTT assay kit for cell viability
• ROS detection kit (DCFDA-based)
• Glutathione assay kit
• Caspase-3 activity assay kit

Methodology:

• Cell Culture and Treatment:
  • Maintain HepG2 cells in DMEM with 10% FBS
  • Pre-treat cells with natural compounds (1-50μM) for 12 hours
  • Expose to sodium arsenite (10-100μM) for 24 hours
  • Include controls (untreated, arsenic-only, compound-only)

• Cytotoxicity Assessment:

  • Measure cell viability using MTT assay according to manufacturer protocol
  • Assess membrane integrity via LDH release assay
  • Determine apoptotic cells by Annexin V/PI staining and flow cytometry

• Oxidative Stress Parameters:

  • Measure intracellular ROS levels using DCFDA fluorescence
  • Quantify glutathione levels (GSH/GSSG ratio) using commercial kit
  • Assess lipid peroxidation via malondialdehyde (MDA) measurement

• Mechanistic Studies:

  • Evaluate antioxidant enzyme activities (SOD, catalase, GPx)
  • Analyze protein expression of Nrf2 and downstream targets by western blot
  • Assess mitochondrial membrane potential using JC-1 staining

Data Analysis: Statistical analysis should compare arsenic-only groups with natural compound pre-treatment groups to determine significant protective effects. A successful intervention would show dose-dependent improvement in viability, reduced oxidative stress markers, and normalized antioxidant defense parameters.

Integrated Approach: The Scientist's Toolkit

Successful optimization of natural products requires specialized reagents and methodologies that span disciplinary boundaries.

Table 3: Essential Research Reagent Solutions for Natural Product Optimization

Reagent/Material	Function	Application Examples
NHS-Activated Beads	Covalent immobilization of natural products for affinity purification	Target identification via CCCP [73]
Click Chemistry Reagents	Bioorthogonal conjugation for probe synthesis and labeling	Azide-alkyne cycloaddition for ABPP probes [78]
Spray Drying Excipients	Particle engineering and stabilization	Lactose, mannitol, phospholipids for DPI formulations [76]
Lipid Nanoemulsion Components	Solubilization and delivery enhancement	Medium-chain triglycerides, lecithin, poloxamers [74]
Thermal Shift Dyes	Protein stability monitoring in label-free target engagement	CETSA and TPP experiments [78]
Antioxidant Assay Kits	Quantification of oxidative stress parameters	Evaluation of toxicity mitigation [79]

The optimization of natural products for enhanced bioavailability, target specificity, and reduced toxicity represents a multidisciplinary challenge at the intersection of chemical biology, pharmaceutical sciences, and systems biology. The strategies outlined in this technical guide provide a framework for advancing natural product research from phenomenological observation to mechanism-based therapeutic development.

Future directions in the field will likely include the increased integration of artificial intelligence for predicting optimal modification sites, the development of more sophisticated delivery systems with triggered release capabilities, and the application of single-cell proteomics for understanding cell-type-specific targeting. Furthermore, the systematic investigation of natural product combinations, inspired by traditional medicine practices, may reveal synergistic effects that enhance efficacy while minimizing individual compound toxicity.

As natural products continue to provide invaluable starting points for therapeutic development, the systematic optimization approaches described herein will be essential for translating nature's chemical diversity into the next generation of precision medicines.

The field of natural products research is at a pivotal crossroads, where contemporary bioinformatic and chemoinformatic capabilities hold immense promise for reshaping knowledge management, analysis, and data interpretation [80]. Research in this domain increasingly relies on a disparate set of non-standardized, insular, and specialized databases, which presents a series of fundamental challenges for both internal data access and integration with related fields [80]. The complexity and volume of heterogeneous data in life sciences research necessitate good documentation, processing, and standardization—yet in practice, a significant gap exists between this need and reality [81]. Routinely collected scientific data are often incomplete or irretrievable, with limited knowledge of and adherence to data and metadata standards among researchers [81].

The core challenge lies in the architectural and philosophical differences between major databases serving the natural products community. While large, well-structured databases exist that focus individually on chemical structures (e.g., PubChem with over 100 million entries) or biological organisms (e.g., GBIF with over 1.9 billion entries), the scarce interlinkages between these resources severely limit their application for comprehensive documentation of natural product occurrences [80]. This fragmentation breaks the crucial evidentiary link required for tracing information back to original data sources and assessing quality [80]. Within this landscape, three databases—NPCDR, LOTUS, and ChEMBL—represent critical resources with complementary strengths and distinct data architectures that must be harmonized for systematic analysis.

Database-Specific Architecture and Challenges

Core Characteristics and Technical Specifications

Table 1: Core Database Characteristics and Technical Specifications

Database	Primary Focus	Data Architecture	Core Data Unit	License & Access
LOTUS	Natural products occurrence	Wikidata-based knowledge graph; mirrored at lotus.naturalproducts.net	Referenced structure-organism pairs (750,000+)	CC0 (Creative Commons 0)
ChEMBL	Bioactive molecules with drug-like properties	Manually curated relational database	Chemical, bioactivity, and genomic data	Freely accessible
NPCDR	Not sufficiently detailed in search results	Not sufficiently detailed in search results	Not sufficiently detailed in search results	Not sufficiently detailed in search results

LOTUS: Transforming Natural Products Knowledge Management

The LOTUS initiative represents a transformative approach to natural products knowledge management, building on the experience gained through the establishment of the COlleCtion of Open NatUral producTs (COCONUT) regarding the aggregation and curation of natural products structural databases [80]. This expertise was expanded to accommodate biological organisms and scientific references, resulting in the standardization of pairs characterizing a natural product occurrence at the chemical, biological, and reference levels after extensive data curation and harmonization of over 40 electronic resources [80]. LOTUS disseminates 750,000+ referenced structure-organism pairs, representing an intensive preliminary curatorial phase and a significant step toward providing a high-quality, computer-interpretable knowledge base [80].

A fundamental innovation of the LOTUS initiative is its hosting on the Wikidata platform, which broadens data access and interoperability while opening new possibilities for community curation and evolving publication models [80]. This strategic decision applies both FAIR (Findability, Accessibility, Interoperability, and Reuse) and TRUST (Transparency, Responsibility, User focus, Sustainability and Technology) principles to natural products knowledge management [80]. The Wikidata framework contains over 1 billion statements in the form of subject-predicate-object triples that are machine-interpretable and can be enriched with qualifiers and references [80]. However, this approach has notable drawbacks: the SPARQL query language, while powerful, can be intimidating for less experienced users, and typical queries of molecular electronic natural products resources such as structural or spectral searches are not yet available in Wikidata [80].

ChEMBL: Bioactive Molecule Data for Drug Discovery

ChEMBL serves a distinctly different purpose as a manually curated database of bioactive molecules with drug-like properties [82]. It brings together chemical, bioactivity, and genomic data to aid the translation of genomic information into effective new drugs [82]. As a traditional relational database with regular updates (e.g., ChEMBL 36 [83]), it provides highly structured, quality-controlled data on compound activities against biological targets. This focus makes it invaluable for drug discovery workflows but creates integration challenges with natural product-centric resources like LOTUS due to differing data models and prioritization.

Data Integration Hurdles and FAIR Principles

The integration of these disparate databases faces multiple significant hurdles. The fundamental challenge lies in the differing data architectures—Wikidata-based knowledge graph (LOTUS) versus traditional relational database (ChEMBL)—which require distinct querying approaches and integration methodologies [80] [82]. Additionally, the scope and focus of each database varies considerably: LOTUS aims to be cross-kingdom and comprehensive for natural product occurrences, while ChEMBL focuses specifically on compounds with drug-like properties and bioactivity data [80] [82].

Data quality and curation methodologies present another significant integration hurdle. LOTUS employs automated harmonization supplemented with community curation, while ChEMBL relies on manual curation by experts, leading to potential differences in data reliability and consistency [80] [82]. Furthermore, identifier mapping between databases remains challenging, as each resource may use different chemical, organism, and reference identifiers without consistent cross-referencing [80].

Database Integration Workflow

Methodological Framework for Cross-Database Integration

Experimental Protocol for Data Extraction and Harmonization

The complex process of data extraction and harmonization across multiple databases requires a systematic, step-by-step approach to ensure data integrity and interoperability. This protocol adapts methodologies from complex systematic review data extraction and applies them to the natural products domain [84].

Phase 1: Database Planning

• Step 1: Determine Data Items - Identify which specific data elements need to be collected from each database to answer the research question(s). Previous knowledge of the topic area, samples of key articles, and previously conducted related analyses can help identify pertinent data items [84].
• Step 2: Group Data Items into Distinct Entities - Logically group identified data items according to their relevance and position in the hierarchy into entities that will be translated into database tables. Create a tree diagram where the root entity captures data that occur once (e.g., compound characteristics), with branch entities for repeated data (e.g., multiple biological activities) [84].
• Step 3: Specify Relationships Among Entities - Define how each pair of entities connects through one-to-one (1:1), one-to-many (1:M), or many-to-many (M:M) relationships, depending on how instances in the first entity relate to instance(s) in the second entity [84].

Phase 2: Database Building

• Step 4: Create the Database Structure - Implement the planned structure using appropriate database management systems, creating tables corresponding to each entity with defined primary and foreign keys to maintain referential integrity [84].
• Step 5: Implement Data Validation Mechanisms - Incorporate real-time validation mechanisms covering data type, plausibility, and logical checks during data import and entry to ensure data integrity [81].
• Step 6: Develop User Interface - Create an intuitive graphical interface using modern web technologies (HTML5/CSS) that allows researchers to interact with the integrated data without requiring deep technical expertise [81].

Phase 3: Data Manipulation

• Step 7: Pilot Data Extraction - Test the extraction and harmonization process with a subset of data from each source database, refining the approach based on encountered challenges [84].
• Step 8: Execute Full Data Extraction - Extract the complete dataset from each source database using appropriate query methods (SPARQL for LOTUS, SQL for ChEMBL) [80] [82].
• Step 9: Compare and Resolve Discrepancies - Systematically compare datasets extracted from different sources to identify and resolve discrepancies in compound identifiers, organism taxonomy, or activity measurements [84].
• Step 10: Export Integrated Dataset - Format the final harmonized dataset for analysis in statistical software or specialized natural products research platforms [81] [84].

Implementation Example Using Open-Source Tools

The implementation of this methodological framework can be achieved using open-source software to ensure broad accessibility. For database building and data manipulation phases, Epi Info provides capabilities for creating relational databases and data validation features that can be adapted for complex natural products data integration projects [84]. This can be supplemented with R libraries for specialized data comparison and discrepancy resolution tasks [84].

For querying the Wikidata-based LOTUS data, the SPARQL protocol provides powerful access despite its steep learning curve [80]. To address this challenge, the LOTUS initiative maintains a parallel hosting solution at https://lotus.naturalproducts.net (LNPN) within the naturalproducts.net ecosystem, providing a more user-friendly interface with tailored search modes for the natural products research community [80].

Data Relationship Mapping

The Researcher's Toolkit: Essential Solutions for Database Integration

Table 2: Research Reagent Solutions for Database Integration

Tool/Resource	Function	Application in Integration
Wikidata Platform	Collaborative knowledge graph	Hosts LOTUS data with cross-disciplinary and multilingual support; contains >1 billion machine-interpretable statements [80]
SPARQL Query Language	Semantic query language	Retrieves and manipulates data stored in Resource Description Framework (RDF) format; essential for querying LOTUS Wikidata instance [80]
FAIR Principles	Data management guidelines	Ensures data are Findable, Accessible, Interoperable, and Reusable; provides framework for evaluating integration approaches [81] [80]
HL7 Clinical Document Architecture	Data interchange standard	Facilitates seamless data exchange with other healthcare systems; supports importing and exporting data [81]
Epi Info	Database building software	Creates relational databases with data validation features; useful for complex data extraction projects [84]
R Libraries	Statistical programming	Facilitates data comparison and resolves discrepancies in extracted datasets [84]

The integration of diverse natural products databases represents both a formidable challenge and a tremendous opportunity for advancing chemical biology and systematics research. The methodological framework presented here provides a structured approach to overcoming the technical and architectural hurdles inherent in combining resources like LOTUS, ChEMBL, and other domain-specific databases. As the field continues to evolve, emphasis on FAIR and TRUST principles will be essential for developing next-generation natural products knowledge bases that are truly interoperable and capable of supporting the complex, transdisciplinary research questions that define modern chemical biology [81] [80].

Future developments in this space will likely focus on enhanced community curation models, improved automated harmonization techniques, and more sophisticated identifier mapping services that reduce the manual effort required for cross-database integration. The LOTUS initiative's approach of leveraging Wikidata while maintaining a domain-specific portal offers a promising template for how specialized research communities can balance the competing demands of accessibility, interoperability, and specialized functionality [80]. As these infrastructures mature, they will increasingly enable researchers to move beyond siloed analysis toward truly integrated systematic exploration of natural products chemistry and biology.

From Correlation to Cure: Validating Efficacy and Clinical Potential

The escalating crisis of antimicrobial resistance necessitates the discovery of novel bioactive compounds. Within microbial natural product research, a long-standing hypothesis posits that phylogenetic distance correlates with secondary metabolite diversification. This case study examines foundational and contemporary evidence from the order Myxococcales (myxobacteria) that systematically validates this taxonomy-chemical diversity link. We present a detailed analysis of a landmark mass spectrometry-based metabolomics study of approximately 2,300 strains, which provided statistical evidence that the chances of discovering novel metabolites are significantly greater by examining strains from new genera rather than additional representatives within the same genus [16]. Supported by genomic and experimental data, this paradigm establishes a strategic framework for prioritizing microbial resources in future drug discovery pipelines.

In the search for uncharacterized, medicinally relevant natural products, a central challenge is improving the efficiency of discovery and avoiding the recurrent isolation of known compounds [16]. The phylum Myxococcota (hereafter referred to by its common name, myxobacteria) represents a prolific source of secondary metabolites with unique scaffolds and potent biological activities [85] [86]. These Gram-negative δ-proteobacteria are distinguished by their multicellular social behaviors, predatory lifestyles, and exceptionally large genomes, which are enriched with biosynthetic gene clusters (BGCs) [86] [87].

The fundamental premise of the taxonomy-chemical diversity link is that evolutionary divergence, reflected in taxonomy, drives the diversification of biosynthetic pathways and their small molecule products. Consequently, exploring phylogenetically distant taxa should yield a greater proportion of chemical novelty than intensive sampling within a single genus or species. This case study dissects the experimental evidence that firmly establishes myxobacteria as a model system for validating this principle.

Metabolomic Validation: A Large-Scale MS-Based Study

Experimental Methodology and Workflow

A seminal study undertook a systematic metabolite survey of ~2,300 myxobacterial strains to investigate the correlation between taxonomy and metabolome profile [16]. The experimental protocol was designed for high-throughput consistency and comparative analysis.

• Strain Selection & Cultivation: A diverse collection of ~2,300 strains from the order Myxococcales was selected to achieve high coverage of known myxobacterial taxonomy. Strains were cultivated in empirically optimized, genus-typical media to support robust secondary metabolism [16].
• Standardized Metabolite Extraction: Cellular metabolites were extracted from all strains using standardized protocols to ensure reproducibility and minimize technical variation [16].
• LC-MS Analysis & Data Processing: All extracts were analyzed using Liquid Chromatography-Mass Spectrometry (LC-MS) under uniform conditions. The resulting data sets were processed to identify mass spectrometric features (defined by m/z, retention time, and isotope pattern) corresponding to both known and previously unidentified metabolites [16].
• Dereplication & Annotation: An in-house database containing 170 structurally characterized myxobacterial metabolite families (comprising 398 individual compounds) was used to annotate known compounds in the extracts based on accurate m/z, retention time, and isotope pattern matching [16].
• Data Clustering & Statistical Analysis: Annotation results were grouped according to suborder, family, and genus to create compound distribution matrices. Hierarchical clustering of individual, blinded LC-MS data sets was performed to determine if chemical profiles self-organized according to taxonomic relationships [16].

The following diagram illustrates this integrated experimental workflow:

Key Findings and Quantitative Data

The analysis yielded compelling, data-driven evidence for the taxonomy-chemical diversity link.

• Genus-Specific Metabolite Production: The creation of a compound distribution heatmap revealed a striking pattern: a significant subset of known metabolite families was either unique or highly specific to a particular genus (Figure 2c in [16]). This established the existence of distinct chemotypes at the genus level.
• Hierarchical Clustering by Chemotype: When 50 blinded data sets from each of the seven most frequent genera were subjected to hierarchical clustering based solely on their known metabolite profiles, the data sets self-organized into genus-specific clades (Figure 3a in [16]). This demonstrated that inter-genera variations in the secondary metabolome are significant and measurable.
• The "Taxonomy Paradigm": The study concluded that the probability of discovering novel natural product scaffolds is maximized by exploring new genera rather than by further sampling within the same genus. This principle was subsequently supported by the discovery of new compounds like jahnellamides and aetheramides from the then-lesser-explored genera Jahnella and Aetherobacter [16].

Table 1: Summary of Key Quantitative Findings from the Metabolomics Study [16]

Metric	Finding	Implication
Strains Analyzed	~2,300	Large-scale, statistically robust analysis
Known Compounds Database	170 families (398 compounds)	Comprehensive basis for dereplication
Genus-Specific Clustering	Data sets self-organized into genus-level clades	Chemotype is a strong reflection of genotype/taxonomy
Proposed Discovery Strategy	Focus on novel genera over novel species within a genus	"Taxonomy Paradigm" for efficient discovery

Genomic Corroboration: Insights from Biosynthetic Gene Clusters

Genome mining provides a complementary line of evidence that reinforces the metabolomic findings. The rich BGC content of myxobacteria has been extensively surveyed.

• Abundance of Unexplored BGCs: An analysis of 994 BGCs from 36 sequenced myxobacteria found that 85% had less than 75% similarity to characterized clusters in the MIBiG repository [87]. This indicates a vast reservoir of biosynthetic potential that remains untapped, consistent with the metabolomic observation of many uncharacterized compounds.
• Taxonomic Distribution of BGCs: Pan-genome analysis of 195 myxobacterial genomes has begun to identify BGCs that are conserved across the phylum, as well as those that are taxon-specific [88]. This genomic diversity underlies the observed chemical diversity, suggesting that evolutionary pressures have shaped specialized metabolism in a lineage-specific manner.
• Chromosomal Organization: Comparative genomics of novel myxobacterial isolates suggests that the spatial proximity of hybrid, modular BGCs on the chromosome may contribute to metabolic adaptability and the generation of chemical diversity [86].

Table 2: Biosynthetic Gene Cluster Diversity in Sequenced Myxobacteria [87]

BGC Class	Number Identified	Representative Annotated Metabolites
Type I PKS (t1PKS)	64	Epothilone, Ambruticin [89]
NRPS	125	Myxochelin
Hybrid PKS-NRPS	166	Myxoprincomide
Ribosomally synthesized and post-translationally modified peptides (RiPPs)	245	-
Terpene	149	Geosmin
Others & Hybrids	185	-

The Scientist's Toolkit: Essential Research Reagents and Methods

Research into myxobacterial natural products relies on a specific set of methodological approaches and reagents.

Table 3: Key Research Reagent Solutions for Myxobacterial Natural Product Studies

Reagent / Method	Function / Application	Key Considerations
Genus-Typical Cultivation Media	Supports growth and secondary metabolism of diverse myxobacteria.	Media composition is empirically optimized; critical for activating BGCs [16].
LC-HRMS/MS Systems	High-resolution metabolite profiling and untargeted discovery.	Enables detection of knowns and unknowns; essential for large-scale metabolomics [16].
antiSMASH Software	In silico identification and analysis of BGCs in genomic data.	Standard tool for genome mining; predicts BGC class and novelty [87].
Electroporation Method for Sorangium	Genetic manipulation of a prolific but genetically intractable genus.	Enables targeted gene knockout (e.g., using crtB reporter) to elucidate biosynthetic pathways [89].
BIG-SCAPE-CORASON Platform	Generating sequence similarity networks of BGCs.	Analyzes biosynthetic diversity and clusters BGCs into Gene Cluster Families (GCFs) [87].

The large-scale metabolomic and genomic evidence from myxobacteria provides a robust validation of the link between taxonomic distance and chemical diversity. The "taxonomy paradigm" [16] offers a strategic blueprint for future natural product discovery, directing efforts toward the isolation and characterization of phylogenetically novel organisms.

Future research will be propelled by several key fronts:

• Isolation of Novel Taxa: Continued bioprospecting in underexplored environments (e.g., South African biomes [90]) is crucial for accessing truly novel chemotypes.
• Genetic Tool Development: Advanced genetic engineering methods, like the recently established electroporation protocol for Sorangium cellulosum [89], are essential for elucidating the function of cryptic BGCs.
• Integrated Omics: Combining genomics, metabolomics, and metagenomics will continue to bridge the gap between biosynthetic potential and observed chemical output, helping to solve the "great biosynthetic gene cluster anomaly" [91].

In conclusion, myxobacteria serve as a powerful model system that empirically confirms a core principle in chemical biology. By leveraging taxonomic guidance, researchers can optimize discovery pipelines to unveil the next generation of natural product-based therapeutic leads.

Natural products, derived from plants, microbes, and marine organisms, have served as a cornerstone for drug discovery, providing unique structural diversity and potent bioactivity. Their historical significance is underscored by their continuous contribution to pharmacotherapy, particularly in oncology and infectious diseases. In the face of escalating challenges such as antimicrobial resistance (AMR) and the complexity of cancer, natural products offer innovative solutions through multi-target mechanisms, the ability to circumvent established resistance pathways, and their role as inspirations for synthetic analogues. This whitepaper details the mechanisms, successes, and future directions of natural product-derived drugs, emphasizing their integral role within modern chemical biology and systematics research. By leveraging advanced technologies in genomics, metabolomics, and synthetic biology, the field is experiencing a revitalization, positioning natural products as crucial agents in addressing some of the most pressing issues in modern medicine.

Natural products (secondary metabolites) are small molecules produced by biological sources that are not strictly essential for the growth, development, or reproduction of an organism but often provide a competitive advantage in its native environment [92]. From a systems biology perspective, the biosynthesis of these compounds represents an expression of an organism's individuality, shaped by evolutionary pressure and ecological interactions [93] [92]. The chemical diversity of natural products arises from a limited set of biosynthetic building blocks—primarily acetyl coenzyme A (acetyl-CoA), shikimic acid, mevalonic acid, and 1-deoxyxylulose-5-phosphate—which are channeled through countless pathways involving reactions like alkylation, decarboxylation, aldol, and Claisen condensations [92].

The study of natural products is inherently interdisciplinary, bridging chemistry, biology, ecology, and medicine. Systematics research provides the framework for understanding the phylogenetic distribution of biosynthetic gene clusters, while chemical biology investigates the mechanisms by which these small molecules modulate complex biological systems [94]. This synergistic approach has been profoundly successful, with over 23,000 natural compounds identified since the discovery of penicillin, serving as invaluable resources for medicine, agriculture, and industry [93].

Natural Products in Combatting Antimicrobial Resistance

The AMR Crisis and the Natural Product Solution

Antimicrobial resistance (AMR) represents a critical global health challenge, with projections estimating it could cause up to 10 million deaths annually by 2050 if current trends persist [93]. The rise of multidrug-resistant (MDR) pathogens, particularly the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.), underscores the urgent need for novel therapeutic approaches [93]. Bacteria employ multiple strategies to evade antibiotic effects, including enzyme production (e.g., β-lactamases), efflux pump activation, target site alterations, and biofilm formation [93].

Natural products present a promising solution to AMR through several advantages:

• Evolutionary Optimization: Shaped by millennia of evolutionary pressure, they often target multiple bacterial pathways simultaneously, reducing the likelihood of resistance development [93].
• Structural Diversity: They provide unique chemical scaffolds not typically found in synthetic compound libraries, enabling the targeting of novel bacterial pathways [21].
• Synergistic Potential: They can potentiate the effects of conventional antibiotics and slow the development of resistance when used in combination [93] [95].

Approximately 30-50% of existing drugs are derived from medicinal plants, highlighting their continued importance in anti-infective drug discovery [95].

Success Stories and Key Compounds

Table 1: Representative Natural Product-Derived Antimicrobial Agents

Natural Product/Drug	Natural Source	Class	Mechanism of Action	Target Pathogens
Penicillins	Penicillium fungi	β-lactam antibiotic	Cell wall synthesis inhibition	Broad-spectrum, including susceptible Staphylococci
Cephalosporins	Cephalosporium acremonium	β-lactam antibiotic	Cell wall synthesis inhibition	Broad-spectrum, including some β-lactamase producers
Tetracyclines	Streptomyces bacteria	Polyketide	Protein synthesis inhibition (30S ribosomal subunit)	Broad-spectrum, including intracellular pathogens
Vancomycin	Amycolatopsis orientalis	Glycopeptide antibiotic	Inhibits cell wall synthesis (binds D-Ala-D-Ala)	MRSA, other Gram-positive infections
Melittin	Bee (Apis mellifera) venom	Antimicrobial peptide (AMP)	Membrane disruption	MRSA [93]
Berberine	Barberry plants	Alkaloid	Multiple targets including cell membrane and biofilm interference	Wide range of bacteria [93]
Allicin	Garlic	Organosulfur compound	Reacts with thiol groups, enzyme inhibition	Wide range of bacteria, fungi [93]

Systematic reviews have identified numerous plant-derived compounds with significant activity against WHO priority pathogens. The most promising classes of bioactive compounds include alkaloids, flavonoids, phenols, saponins, tannins, and terpenoids [96]. Among these, flavonoids represent approximately 24.8% of the antioxidant product derivatives examined for antimicrobial activity [96]. These compounds are typically extracted using various solvents, including ethanol, methanol, aqueous solutions, benzoate, ethyl acetate, and n-butanol from different plant parts such as leaves, bark, flowers, and roots [96].

Experimental Protocols for Evaluating Antimicrobial Activity

Protocol 1: Standard Broth Microdilution for MIC Determination

• Preparation of Test Compounds: Dissolve natural product extracts or pure compounds in appropriate solvents (DMSO is commonly used with final concentration ≤1%) and prepare serial two-fold dilutions in cation-adjusted Mueller-Hinton broth.
• Inoculum Preparation: Adjust bacterial suspension to 0.5 McFarland standard (approximately 1-2 × 10^8 CFU/mL) in sterile saline, then further dilute in broth to achieve final inoculum density of 5 × 10^5 CFU/mL.
• Microtiter Plate Setup: Dispense 100 μL of diluted compound solutions into 96-well plates. Include growth control (broth + inoculum), sterility control (broth only), and solvent control.
• Inoculation and Incubation: Add 100 μL of bacterial inoculum to each well except sterility control. Cover plates and incubate at 35±2°C for 16-20 hours.
• MIC Determination: The Minimum Inhibitory Concentration (MIC) is the lowest concentration showing no visible growth. Confirm with resazurin dye (0.02% w/v) - blue indicates no growth, pink/colorless indicates bacterial growth.

Protocol 2: Checkerboard Assay for Synergy Testing

• Solution Preparation: Prepare stock solutions of natural product and conventional antibiotic at 10× the highest test concentration.
• Plate Setup: Create a two-dimensional dilution series with varying concentrations of both compounds. One agent is diluted along the x-axis, the other along the y-axis.
• Inoculation: Add bacterial inoculum as in Protocol 1.
• Calculation of FIC Index: After incubation, calculate the Fractional Inhibitory Concentration (FIC) index: FIC index = (MIC of drug A in combination/MIC of drug A alone) + (MIC of drug B in combination/MIC of drug B alone). Interpretation: FIC ≤0.5 = synergy; 0.5-4.0 = indifference; >4.0 = antagonism.

Protocol 3: Time-Kill Assay

• Setup: Prepare flasks containing natural product at relevant concentrations (e.g., 0.5×, 1×, 2× MIC) in broth with approximately 5 × 10^5 CFU/mL bacteria.
• Sampling: Remove aliquots at 0, 2, 4, 6, 8, 12, and 24 hours, serially dilute in neutralizer solution, and plate on appropriate agar.
• Incubation and Analysis: Incubate plates 18-24 hours, count colonies, and plot log10 CFU/mL versus time. Synergy is defined as ≥2-log10 decrease in CFU/mL compared to the most active single agent.

Natural Products in Cancer Therapeutics

Historical Impact and Contemporary Relevance

Natural products have been the single most productive source of leads for anticancer drug discovery. Their structural complexity and diversity enable them to interact with multiple biological targets, making them particularly valuable in addressing the complexity of cancer pathogenesis [97] [98]. Historically, natural products have provided foundational chemotherapeutic agents, with many current cancer drugs being natural products, derived from natural products, or inspired by natural product structures [98] [92].

The developmental pipeline for natural product-based cancer drugs encompasses several stages: (1) resource discovery from terrestrial plants, fungi, and marine organisms; (2) mechanism exploration through in vitro and in vivo models; (3) lead optimization through structural modification; and (4) clinical development [98]. This systematic approach continues to yield novel therapeutic candidates with unique mechanisms of action.

Success Stories and Key Compounds

Table 2: Representative Natural Product-Derived Anticancer Agents

Natural Product/Drug	Natural Source	Class	Mechanism of Action	Cancer Applications
Paclitaxel (Taxol)	Pacific Yew tree (Taxus brevifolia)	Diterpenoid	Microtubule stabilization, mitotic arrest	Ovarian, breast, lung cancers
Camptothecin derivatives (Irinotecan, Topotecan)	Camptotheca acuminata tree	Alkaloid	Topoisomerase I inhibition	Colorectal, ovarian, small cell lung cancer [97] [98]
Vinca Alkaloids (Vinblastine, Vincristine)	Madagascar periwinkle (Catharanthus roseus)	Alkaloid	Microtubule disruption, mitotic arrest	Leukemia, lymphoma, testicular cancer
Podophyllotoxin derivatives (Etoposide, Teniposide)	Mayapple (Podophyllum peltatum)	Lignan	Topoisomerase II inhibition	Testicular, lung cancers, lymphoma
Homoharringtonine	Cephalotaxus genus	Alkaloid	Protein synthesis inhibition, cell cycle arrest	Chronic myeloid leukemia
Narciclasine	Amaryllidaceae plants	Alkaloid	Topoisomerase I inhibition, DNA damage, G2/M arrest	Multiple cancer cell lines [98]
Gnetin C	Gnetum species	Stilbene polyphenol	Targets MTA1/PTEN/Akt/mTOR pathway	Advanced prostate cancer [97]
Marine-derived agents (Bryostatins, Ecteinascidin)	Marine organisms	Various	Various mechanisms including epigenetic modulation	Various cancers [94]

Recent research has identified numerous promising natural product leads with novel mechanisms. For instance, narciclasine was identified as a novel inhibitor of topoisomerase I (acting as a suppressor rather than a poison), potently inhibiting cancer cell proliferation and inducing G2/M phase arrest and apoptosis [98]. Similarly, gnetin C has demonstrated efficacy in targeting the MTA1/PTEN/Akt/mTOR pathway in advanced prostate cancer models [97]. Ten new pentacyclic triterpenoid glycosides from the roots of Ilex asprella have shown moderate cytotoxic activities against H1975 and HCC827 lung cancer cell lines, providing new lead compounds for structural optimization [98].

Key Signaling Pathways Targeted by Natural Products

Natural products frequently target critical oncogenic signaling pathways. The most commonly targeted pathways in cancer include:

• PI3K/Akt/mTOR pathway: A central regulator of cell growth, proliferation, and survival, targeted by compounds such as gnetin C and tanshinone I derivatives [97] [98].
• RAF/MEK/ERK pathway: A key proliferative signaling cascade frequently dysregulated in cancers.
• JAK/STAT pathway: Involved in cytokine signaling and inflammation, contributors to tumorigenesis.
• NF-κB pathway: A critical mediator of inflammatory responses and cell survival.
• Cell cycle checkpoints and apoptosis regulators: Including p53, Bcl-2/Bax, and cyclin-dependent kinases [97].

The following diagram illustrates the key signaling pathways frequently targeted by natural product-derived anticancer agents:

Experimental Protocols for Anticancer Evaluation

Protocol 1: Cytotoxicity Assessment (MTT Assay)

• Cell Seeding: Plate cancer cells in 96-well plates at optimal density (e.g., 5,000-10,000 cells/well) in complete medium and incubate for 24 hours (37°C, 5% COâ‚‚).
• Compound Treatment: Prepare serial dilutions of natural products in DMSO (final concentration ≤0.1%) and add to cells. Include vehicle control and blank (medium only).
• Incubation: Incubate for 24-72 hours depending on experimental design.
• MTT Addition: Add MTT solution (5 mg/mL in PBS) to each well (10% of total volume) and incubate 2-4 hours until purple formazan crystals are visible.
• Solubilization and Measurement: Carefully remove medium, add DMSO to dissolve formazan crystals, and measure absorbance at 570 nm with reference filter at 630 nm.
• ICâ‚…â‚€ Calculation: Calculate percentage viability relative to control and determine half-maximal inhibitory concentration (ICâ‚…â‚€) using nonlinear regression analysis.

Protocol 2: Apoptosis Detection by Annexin V/Propidium Iodide Staining

• Cell Treatment: Treat cells with natural product at relevant concentrations (e.g., ICâ‚…â‚€) for 24-48 hours.
• Cell Harvesting: Collect both adherent and floating cells, wash with cold PBS, and resuspend in 1× binding buffer.
• Staining: Add Annexin V-FITC and propidium iodide according to manufacturer's instructions. Incubate for 15 minutes in the dark at room temperature.
• Flow Cytometry Analysis: Analyze samples within 1 hour using flow cytometry. Measure fluorescence emission at 530 nm (FITC) and >575 nm (PI). Quadrant analysis distinguishes viable cells (Annexin V⁻/PI⁻), early apoptotic (Annexin V⁺/PI⁻), late apoptotic (Annexin V⁺/PI⁺), and necrotic cells (Annexin V⁻/PI⁺).

Protocol 3: Cell Cycle Analysis by Propidium Iodide DNA Staining

• Cell Treatment and Fixation: Treat cells as above, harvest, wash with PBS, and fix in 70% ethanol at -20°C for at least 2 hours.
• Staining: Centrifuge fixed cells, resuspend in PBS containing RNase A (100 μg/mL) and propidium iodide (50 μg/mL). Incubate 30 minutes at 37°C in the dark.
• Flow Cytometry Analysis: Analyze DNA content by flow cytometry, measuring fluorescence at >575 nm. Use appropriate software to determine percentage of cells in G0/G1, S, and G2/M phases of the cell cycle.

Emerging Trends and Future Perspectives

Technological Advancements Driving Innovation

The field of natural product research is experiencing a renaissance driven by several technological advancements:

• Omics Technologies: Genomics, transcriptomics, proteomics, and metabolomics enable comprehensive analysis of biosynthetic pathways and rapid identification of novel compounds [93] [21].
• Advanced Analytical Techniques: High-resolution mass spectrometry (HRMS), coupled LC-MS-NMR, and computational metabolomics accelerate metabolite identification and dereplication [21].
• CRISPR-Cas Technologies: Facilitate genetic manipulation of biosynthetic pathways in producer organisms and creation of more relevant disease models for compound evaluation [93] [21].
• AI and Machine Learning: Enable rational compound screening by filtering large datasets to predict efficacy, synergy, and toxicity, significantly accelerating the discovery process [97].
• Nanoparticle Encapsulation: Advanced drug delivery systems enhance bioavailability, stability, and targeted delivery of natural products, overcoming limitations of poor solubility and rapid metabolism [93] [98].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Research Reagent Solutions for Natural Product Research

Reagent/Technology	Function/Application	Examples in Current Research
LC-HRMS Systems	Metabolite profiling, dereplication, structural characterization	UHPLC-Q-TOF systems for comprehensive metabolome annotation [21]
NMR Spectroscopy	Structural elucidation, compound identification	Combined LC-MS-SPE-NMR for unknown metabolite identification [21]
Global Natural Products Social Molecular Networking (GNPS)	Mass spectrometry data sharing, dereplication, analog discovery	Community curation of mass spectrometry data for natural products [21]
CRISPR-Cas Systems	Gene editing in producer organisms, target validation	Engineering of biosynthetic pathways; creation of disease models [93] [21]
High-Content Screening Systems	Phenotypic screening with multiparametric analysis	Identification of compounds with complex mechanisms of action [21]
Nanoparticle Delivery Systems	Enhanced bioavailability, targeted delivery, reduced toxicity	Naringin-dextrin nanocomposites showing enhanced efficacy against lung carcinogenesis [97]
3D Cell Culture Models	More physiologically relevant in vitro testing	Organoid cultures for cancer drug screening [97]

Challenges and Future Directions

Despite the promising potential of natural products, several challenges remain:

• Bioavailability and Pharmacokinetics: Many natural products suffer from poor solubility, stability, and rapid metabolism, requiring formulation advancements [93] [97].
• Supply and Sustainability: Sustainable sourcing of natural products, especially from rare or slow-growing organisms, presents challenges that can be addressed through synthesis, cultivation, or heterologous production [93] [94].
• Standardization and Reproducibility: Variable composition of natural extracts necessitates rigorous standardization and quality control [93].
• Complexity of Mechanism of Action: The multi-target nature of many natural products complicates mechanistic studies and regulatory approval [93].

Future research directions will likely focus on:

• Integration with Immunotherapy: Exploring natural products that influence immune checkpoints and the tumor microenvironment [97].
• Personalized Medicine Approaches: Genomic and molecular stratification to guide natural product-based therapies [97].
• Microbiome Interactions: Investigating the role of microbiota in modulating the efficacy and metabolism of natural products [97].
• Combination Therapies: Rational design of natural product-conventional drug combinations to enhance efficacy and overcome resistance [93] [97].

The following diagram illustrates a modern workflow for natural product-based drug discovery, integrating traditional and advanced technological approaches:

Natural products continue to demonstrate immense value in addressing two of the most challenging areas in modern medicine: antimicrobial resistance and cancer. Their evolutionary optimization, structural diversity, and multi-target mechanisms position them uniquely to overcome resistance mechanisms that plague conventional therapies. While challenges in bioavailability, sustainable supply, and mechanistic characterization remain, technological advancements in omics, analytics, bioengineering, and AI are rapidly addressing these limitations. The future of natural product-based drug discovery lies in the intelligent integration of traditional knowledge with cutting-edge technologies, creating a virtuous cycle of discovery, optimization, and development. As the field continues to evolve, natural products will undoubtedly remain an essential component of the therapeutic arsenal, providing innovative solutions to combat the global health challenges of AMR and cancer.

Natural products (NPs) and combinatorial libraries represent two foundational pillars of modern drug discovery. NPs, derived from plants, microorganisms, and marine organisms, have evolved over millions of years to interact with biological systems, serving as a historical cornerstone for therapeutic development [1]. In contrast, combinatorial libraries are a technological achievement, enabling the systematic synthesis and screening of millions to billions of synthetic compounds (SCs) to identify novel drug candidates [99]. This review provides a comprehensive technical comparison of these approaches, examining their structural characteristics, discovery methodologies, and respective roles in addressing contemporary challenges in pharmaceutical development, particularly within the framework of chemical biology and systematics research.

Structural and Physicochemical Properties

The structural divergence between natural products and synthetic compounds from combinatorial libraries significantly influences their biological interactions and drug-likeness.

Table 1: Comparative Analysis of Structural Properties between Natural Products and Synthetic Compounds

Property	Natural Products (NPs)	Synthetic Compounds (SCs)
Molecular Complexity	Higher molecular complexity, more sp³-hybridized carbon atoms, increased stereocenters [1]	Generally lower molecular complexity, more planar structures [100]
Structural Frameworks	More oxygen atoms, ethylene-derived groups, unsaturated systems, and aliphatic rings [100]	More nitrogen atoms, sulfur atoms, halogens, and aromatic rings (e.g., phenyl) [100]
Ring Systems	Larger, more diverse, and more complex ring systems; bigger fused rings; more non-aromatic rings [100]	Prevalent use of five- and six-membered rings; more aromatic rings; recent increase in four-membered rings [100]
Physicochemical Trends	Increasing molecular size and hydrophobicity over time; higher structural diversity and uniqueness [100]	Constrained structural evolution governed by drug-like rules and synthetic accessibility [100]

This structural dichotomy translates into distinct bioactivity profiles. The elevated complexity and three-dimensionality of NPs facilitate interactions with complex biological targets, such as protein-protein interfaces, which are often intractable for flatter synthetic molecules [1]. Furthermore, NPs often possess "privileged structures" honed by evolution for specific biological functions, such as defense or signaling [1]. For instance, many NPs violate Lipinski's Rule of Five yet exhibit excellent oral bioavailability, challenging traditional drug-likeness paradigms [1] [21]. In contrast, SCs are typically designed with strict adherence to these rules, ensuring favorable pharmacokinetic properties but potentially limiting structural novelty and target diversity [100].

Drug Discovery Workflows and Methodologies

The processes for discovering bioactive leads from natural products and combinatorial libraries involve distinct philosophies, techniques, and challenges.

Natural Product Drug Discovery

NP discovery leverages nature's biosynthetic machinery, focusing on the isolation and identification of bioactive compounds from complex biological matrices.

Table 2: Key Methodologies in Natural Product-Based Drug Discovery

Methodology	Description	Key Applications
Genome Mining	Computational identification of biosynthetic gene clusters (BGCs) in microbial genomes to predict novel NP pathways [1] [6]	Tools like antiSMASH and DeepBLC enable the discovery of "cryptic" metabolites not produced under standard lab conditions [1].
Sustainable Sourcing	Use of optimized cultivation, microbial fermentation, and plant cell cultures to obtain NPs without depleting natural resources [1]	Overcomes challenges of overharvesting and ensures a scalable, eco-friendly supply of bioactive compounds [1].
Metabolomics & Dereplication	Combination of LC-MS/MS, NMR, and platforms like Global Natural Products Social Molecular Networking (GNPS) for rapid compound identification [1] [21]	Accelerates the differentiation of novel compounds from known entities, streamlining the isolation process [1].
Biosynthetic Engineering	Genetic manipulation of BGCs in native or heterologous hosts to produce novel analogues or optimize titers [6]	Creation of "new-to-nature" products and engineered strains producing a single, improved metabolite (e.g., pseudomonic acid C) [6].

Figure 1: Experimental Workflow for Natural Product Drug Discovery.

A key application of biosynthetic engineering is exemplified in the optimization of the antibiotic mupirocin. Gene knock-out experiments in Pseudomonas fluorescens elucidated the biosynthetic pathway and enabled the creation of a strain that produces exclusively pseudomonic acid C, a more stable and potent analogue of the native mixture [6]. Furthermore, novel enzymatic pathways, such as the one involving a non-canonical Ca²⁺-binding motif in dilarmycins, continue to be discovered, expanding the toolbox for bioengineering [1].

Combinatorial Library Drug Discovery

Combinatorial chemistry employs synthetic strategies to generate vast molecular libraries, prioritizing speed and scale for high-throughput screening.

Table 3: Key Methodologies in Combinatorial Library-Based Drug Discovery

Methodology	Description	Key Applications
Split-and-Pool Synthesis	Solid-phase synthesis method where resin beads are split, reacted with different building blocks, and mixed repeatedly [99]	Enables exponential library growth; a single synthesis with 1,000 building blocks over 3 cycles yields 1 billion compounds [99].
DNA-Encoded Libraries (DELs)	Each small-molecule building block is tagged with a unique DNA sequence, allowing for combinatorial synthesis in solution and identification via DNA sequencing [99]	Facilitates the affinity-based screening of billion-member libraries without the need for physical separation [99] [101].
High-Throughput Screening (HTS)	Automated screening of large compound libraries (individual compounds in microtiter plates) against a biological target [99]	A traditional workhorse; can screen ~100,000 compounds per day, though screening 1 billion compounds would take ~27 years [99].
Parallel Synthesis	Simultaneous, independent synthesis of multiple compounds in an array format (e.g., 96-well plates) [99]	Ideal for producing smaller, focused libraries for structure-activity relationship (SAR) studies [99].

Figure 2: The Split-and-Pool Combinatorial Synthesis Workflow.

The efficiency of combinatorial chemistry is transformative. Synthesizing a library of 1 billion compounds using the split-and-pool method requires only about 3,000 coupling steps and costs approximately $200,000. In stark contrast, synthesizing the same number of compounds via parallel synthesis would require 3 billion coupling steps, take over 2,000 years on a standard synthesizer, and cost between $0.4 and 2 million for just 1 million compounds [99]. Computational tools like CoLiNN are now emerging to visualize the chemical space of these vast libraries without the need for exhaustive compound enumeration, further accelerating the design process [101].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Key Research Reagent Solutions for Drug Discovery

Tool / Reagent	Function	Application Context
Microtiter Plates	Multi-well plates (96 to 6144 wells) for parallel chemical and biological assays [99]	Foundation for HTS and parallel synthesis; enables miniaturization and automation.
Functionalized Solid Supports	Insoluble resins (e.g., polystyrene, controlled pore glass) for solid-phase synthesis [99]	Simplifies purification in split-and-pool and parallel synthesis; allows for use of excess reagents.
DNA Encoding Oligomers	Short DNA sequences that tag individual building blocks during synthesis [99]	Critical for creating and deconvoluting DNA-encoded libraries (DELs).
Biosynthetic Gene Clusters (BGCs)	Contiguous sets of genes encoding a natural product's biosynthetic pathway [1] [6]	Targets for genome mining and heterologous expression to discover or optimize NPs.
Heterologous Hosts (e.g., A. oryzae)	Engineered organisms used to express foreign BGCs [6]	Enables production of NPs from unculturable sources or engineered analogues.
CRISPR-Cas Systems	Precision gene-editing tool [1]	Used for gene knock-outs in NP pathway elucidation and strain engineering.

Synergistic Integration and Future Perspectives

The dichotomy between natural products and combinatorial libraries is increasingly giving way to a synergistic paradigm. Emerging strategies are deliberately blending principles from both fields to create superior platforms for drug discovery.

Pseudo-Natural Products (PNPs) represent a powerful fusion of these worlds. PNPs are synthetic compounds generated by combining NP-derived fragments in novel arrangements not found in nature [102]. This approach aims to merge the biological relevance and structural complexity of NPs with the broad synthetic accessibility and diversity of SCs, populating new regions of chemical space with high potential for bioactivity [102] [100].

Another integrative approach is biology-oriented synthesis (BIOS), which uses core NP scaffolds as starting points for generating focused combinatorial libraries. This ensures that the resulting compounds are pre-validated by evolution for biological relevance while allowing for extensive synthetic exploration of structure-activity relationships [102].

Advanced combinatorial biosynthesis is pushing the boundaries of NP engineering. Research on fungal metabolites like tenellin and bassianin has demonstrated that swapping biosynthetic domains between different pathways can generate a wide array of new metabolites in high yields, revealing the key elements controlling polyketide chain length and methylation [6]. This effectively creates a "combinatorial" approach directly within NP biosynthetic pathways.

Finally, innovative enzymatic-compatible chemistry is being developed to expand the synthetic repertoire. For instance, the development of concerted enzyme-photocatalyst systems enables novel multicomponent biocatalytic reactions, generating molecular scaffolds with rich stereochemistry that were previously inaccessible by either biological or chemical methods alone [103]. This synergy allows for the efficiency and selectivity of enzymes to be combined with the versatility of synthetic photocatalysts.

Natural products and combinatorial libraries offer complementary and often synergistic value in drug discovery. NPs provide unparalleled structural complexity, evolutionary validation, and a high hit rate in screening campaigns, particularly for challenging targets. Combinatorial libraries offer unmatched speed, scale, and synthetic control for lead optimization. The future of drug discovery does not lie in choosing one approach over the other, but in strategically integrating their strengths. Leveraging genomic insights to guide combinatorial design, employing synthetic biology to create novel natural product-inspired libraries, and applying advanced analytics to navigate the combined chemical space will be key to unlocking the next generation of therapeutics. This integrated path forward promises to harness the rich bioactivity of nature's arsenal with the precision and power of modern synthetic and computational methods.

Within the domains of chemical biology and systematics research, natural products (NPs) continue to be indispensable as sources of novel bioactive compounds and chemical scaffolds. It is estimated that between 50–70% of all small-molecule therapeutics in clinical use today are derived from or inspired by natural products [104]. However, a central challenge in modern NP research is the quantitative assessment of bioactivity across different structural classes and biological sources to guide efficient discovery workflows. This necessitates a rigorous framework for analyzing activity landscapes—the complex relationships between chemical structure and biological function—and calculating hit rates, which are critical metrics for prioritizing natural product libraries in drug discovery campaigns [104] [105]. This technical guide provides a systematic overview of the quantitative data, analytical methodologies, and experimental protocols essential for profiling the bioactivity of major natural product classes, contextualized within the broader thesis of harnessing chemical diversity for biological inquiry and systematic classification.

Quantitative Landscape of Natural Product Discovery

Trends in Discovery and Novelty

Retrospective analysis of published microbial and marine-derived natural products from 1941 to 2015 provides critical quantitative insights into the discovery trajectory and inherent novelty of these compounds [104]. The field has witnessed a dramatic increase in output, from a few compounds annually in the 1940s to a plateau of approximately 1,600 new compounds reported per year over the two decades leading up to 2015 [104]. This rise was catalyzed by advancements in separation technologies and spectroscopic methods, particularly the advent of 2D NMR in the mid-1980s [104].

Despite this steady output, metric-based analysis of structural novelty reveals a critical trend. The median maximum Tanimoto similarity score for newly reported compounds relative to previously known structures plateaued at approximately 0.65 by the mid-1990s, a level that persists today [104]. This indicates that the majority of newly discovered natural products have significant structural precedent in the literature.

However, an analysis of compounds with low structural similarity (Tanimoto score < 0.4) shows that an appreciable number of fundamentally unique molecules continue to be discovered each year, underscoring that nature still holds unexplored chemical space, albeit representing a smaller percentage of the total annual output [104]. This duality highlights the necessity for innovative discovery strategies to target these novel chemotypes.

Table 1: Quantitative Trends in Natural Product Discovery (1941-2015)

Metric	Period (1940s)	Period (Mid-1990s - 2015)	Key Implication
Annual Discovery Rate	Few compounds per year	~1,600 compounds per year	The field remains highly productive in terms of raw output [104].
Median Structural Novelty (Tanimoto Score)	Low (data not fully quantified)	Plateaus at ~0.65	Most new compounds have structural precedent; the "low-hanging fruit" may have been harvested [104].
Discovery of Highly Novel Scaffolds (T<0.4)	Not quantified	Appreciable absolute numbers, but decreasing percentage of total	Nature's chemical space is not exhausted; novel chemotypes remain accessible with advanced methods [104].

Bioactivity Hit Rates Across Natural Product Classes

Quantifying the bioactivity "hit rate" of natural product extracts or pure compounds is a fundamental step in prioritizing sources and libraries for further investigation. Hit rates are highly dependent on the assay target, concentration tested, and the definition of a "hit" (e.g., % inhibition of a target). The following table summarizes representative hit rate data and notable bioactive compounds from recent studies across different NP classes.

Table 2: Representative Bioactive Natural Products and Implied Hit Rates from Recent Studies

Natural Product Class / Source	Reported Bioactive Compound(s)	Bioactivity Profile	Implied Hit Rate & Assay Context
Polyphenols (e.g., Hamamelis virginiana)	Complex tannins, flavonoids (quercetin, kaempferol classes)	Potent ROS scavenging, anti-inflammatory (reduced IL-6, IL-1β, TNF-α), ECM-protective (collagenase, elastase inhibition) [106].	Multiple bioactive compounds identified from a single extract, indicating a high hit rate for antioxidant and anti-inflammatory targets in skin cell models [106].
Terpenoids / Essential Oil Components	Linalyl Acetate (encapsulated in γ-CD-MOF)	Core compound widely used in fragrances and cosmetics; study focused on stabilization, not novel bioactivity [106].	N/A for discovery hit rates, but highlights the importance of formulation for bioactivity application.
Plant-derived Flavonoids & Triterpenes (e.g., Dodonaea viscosa)	Compound 12 (unspecified structure); Compound 6 (unspecified structure)	Compound 12: Potent antibacterial vs. Gram-positive bacteria (MIC = 2 μg/mL). Compound 6: Selective antiproliferative effect in inflammatory breast cancer (IBC) cell lines (IC~50~ 4.22-7.73 μM) [106].	Two distinct high-potency hits (antibacterial and anticancer) identified from 13 isolated compounds, suggesting a high hit rate for this medicinal plant extract [106].
Microbial & Marine-derived NPs (General Trend)	N/A	N/A	Analysis suggests that while absolute numbers of novel scaffolds remain stable, the probability of discovering a fundamentally new bioactive scaffold from conventional sources may be decreasing, affecting long-term hit rates for novel entities [104].

Analytical Methodologies for Qualitative and Quantitative Analysis

The accurate quantification of bioactivity and the characterization of active principles rely heavily on sophisticated analytical techniques. Liquid chromatography-mass spectrometry (LC-MS) has become a cornerstone technology in this field [107].

Experimental Protocol: LC-MS Analysis of Phytochemical Constituents

The following workflow details a standard protocol for the qualitative and quantitative analysis of bioactive compounds in plant extracts using LC-MS [107].

1. Sample Preparation:

• Extraction: Use methods such as ultrasonic extraction or pressurized-liquid extraction (PLE) with suitable solvents (e.g., methanol, ethanol, or hydroalcoholic mixtures) to exhaustively extract compounds from dried, powdered plant material [107].
• Clean-up: Employ solid-phase extraction (SPE) to remove interfering pigments, sugars, and other matrix components, thereby reducing matrix effects in the LC-MS analysis [107].

2. Instrumental Analysis:

• Chromatography: Utilize Ultra-High-Performance Liquid Chromatography (UHPLC) with a reversed-phase C18 column (e.g., 2.1 x 100 mm, 1.7 μm particle size) for high-resolution separation. A binary mobile phase (e.g., A: 0.1% formic acid in water; B: acetonitrile) with a gradient elution is standard [107].
• Mass Spectrometry:
  • Ionization: Employ electrospray ionization (ESI) in positive or negative mode for polar molecules, or atmospheric pressure chemical ionization (APCI) for less polar compounds [107].
  • Mass Analyzers:
    • Qualitative/Untargeted Analysis: Use high-resolution tandem mass spectrometers like Quadrupole-Time-of-Flight (Q-TOF) or LTQ Orbitrap for accurate mass measurement and structural elucidation via MS/MS fragmentation [107].
    • Quantitative/Targeted Analysis: Use a Triple Quadrupole (QqQ) mass spectrometer operating in Multiple Reaction Monitoring (MRM) mode for high sensitivity and selective quantification of known compounds [107].

3. Data Acquisition and Processing:

• For untargeted analysis, use information-dependent acquisition (IDA) to automatically trigger MS/MS scans on precursor ions detected in the full scan survey [107].
• Use analytical software to identify compounds by matching accurate mass and fragmentation spectra against databases or standard compounds, and quantify them using internal standard calibration curves [107].

LC-MS Analysis Workflow

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Reagents and Materials for Bioactive Natural Product Analysis

Item / Reagent	Function / Application	Technical Notes
Ultra-High-Performance Liquid Chromatography (UHPLC) System	High-resolution chromatographic separation of complex plant or microbial extracts prior to mass spectrometry.	Enables fast separation with sub-2μm particle columns, providing sharper peaks and higher peak capacity compared to HPLC [107].
High-Resolution Tandem Mass Spectrometer (e.g., Q-TOF, Orbitrap)	Untargeted qualitative analysis; provides accurate mass for elemental composition determination and MS/MS spectra for structural elucidation of unknown bioactive compounds [107].	Crucial for de novo identification of novel natural products and their metabolites in complex biological matrices.
Triple Quadrupole (QqQ) Mass Spectrometer	Highly sensitive and selective targeted quantitative analysis of known bioactive compounds using Multiple Reaction Monitoring (MRM) [107].	The gold standard for validating and quantifying lead compounds in bioactivity assays and pharmacokinetic studies.
Solid-Phase Extraction (SPE) Cartridges (e.g., C18, HLB)	Sample clean-up and pre-concentration of analytes from crude extracts or biological fluids to reduce matrix effects and ion suppression in LC-MS analysis [107].	Essential for improving the accuracy, precision, and robustness of quantitative bioanalytical methods.
Cyclodextrin-Based Metal-Organic Frameworks (CD-MOFs)	Enhanced encapsulation and stabilization of volatile or labile bioactive compounds (e.g., linalyl acetate) for improved shelf-life and controlled release [106].	An advanced material that increases the practical application of volatile bioactive natural products in formulations.

The systematic quantification of bioactivity and mapping of activity landscapes across natural product classes are imperative for advancing their application in chemical biology and drug discovery. While analyses indicate that discovering scaffolds with no structural precedent is becoming statistically more challenging, nature remains a profound source of unique bioactive molecules, as evidenced by the continual identification of potent antibacterial and anticancer agents from diverse sources [106] [104]. The future of the field hinges on the intelligent integration of systematic collection and classification (systematics) with cutting-edge analytical technologies like UHPLC-HRMS and sophisticated data analysis workflows. This integrated approach will enable researchers to more efficiently navigate the complex chemical-biological space of natural products, prioritize the most promising leads, and ultimately unlock new therapeutic opportunities to address unmet medical needs [105] [107].

Natural products (NPs) have historically served as a cornerstone of drug discovery, providing a rich source of structurally complex and biologically active compounds. Their evolutionary optimization for interaction with biological macromolecules makes them particularly valuable for engaging challenging targets, especially protein-protein interactions (PPIs), which have traditionally been considered "undruggable" [1]. In parallel, the field of antibody-drug conjugates (ADCs) has emerged as a transformative therapeutic modality that combines the precision of monoclonal antibodies with the potent cytotoxicity of small molecules, many of which are natural product-derived [108] [109]. This convergence of natural product chemistry and targeted delivery systems represents a paradigm shift in chemical biology and pharmaceutical development.

The structural complexity of natural products, characterized by higher proportions of sp³-hybridized carbon atoms, increased oxygenation, and rigid molecular frameworks, provides unique advantages for modulating complex biological targets [1]. These properties enable NPs to bind to shallow protein surfaces and allosteric sites more effectively than synthetic small molecules, making them ideal starting points for targeting PPIs. Furthermore, their potent bioactivity, honed through millions of years of evolutionary selection, positions them as exceptional payload candidates for ADCs, where maximal cytotoxicity is required at limited intracellular concentrations [105] [109].

This technical review examines the integral role of natural products in addressing these two challenging fronts, with a specific focus on the mechanistic basis for their success, current methodological approaches, and emerging opportunities. By framing this discussion within the broader context of chemical biology and systematics research, we aim to provide researchers and drug development professionals with a comprehensive framework for leveraging natural products in modern therapeutic design.

Natural Products as Privileged Structures for Protein-Protein Interaction Modulation

Chemical and Structural Basis for PPI Engagement

Protein-protein interactions represent a challenging class of therapeutic targets due to their extensive, relatively flat interfaces, which often lack deep binding pockets for conventional small molecules. Natural products have demonstrated remarkable success in modulating PPIs due to several key structural characteristics that differentiate them from synthetic compounds [1]:

• Enhanced Structural Complexity: NPs exhibit greater three-dimensionality with higher fractions of sp³-hybridized carbons and stereochemical complexity, enabling them to bind to irregular protein surfaces.
• Balanced Physicochemical Properties: Despite frequent non-compliance with Lipinski's Rule of Five, many NPs display favorable bioavailability and pharmacokinetic profiles, as evidenced by orally bioavailable NP-derived drugs like artemisinin.
• Evolutionary Optimization: As defense chemicals, signaling agents, and ecological mediators, NPs have been evolutionarily fine-tuned for optimal interactions with biological targets, particularly those involving protein interfaces.

These properties enable natural products to effectively target PPI networks that are often inaccessible to synthetic small molecules, positioning them as privileged scaffolds for this challenging target class.

Methodological Advances in NP-Based PPI Target Identification

Conventional target identification for natural products has been transformed by chemical biology approaches that shift the paradigm from "target-to-drug" to "drug-to-target" [110]. These methodologies leverage active small molecules as probes to directly capture binding proteins from complex biological systems.

Table 1: Advanced Target Fishing Technologies for Natural Product PPI Modulation

Technology	Principle	Application Example	Advantages
Affinity Purification	Uses immobilized NP probes to capture target proteins from cell lysates	Celastrol targeting peroxiredoxins and HO-1 [78]	Direct physical isolation of target complexes
Photoaffinity Labeling	Incorporates photoactivatable groups into NP probes for covalent crosslinking	Ethyl gallate targeting PEBP1 in macrophage activation [78]	Captures transient interactions with spatial resolution
Chemical Proteomics	Combines functionalized NP probes with quantitative mass spectrometry	Withangulatin A targeting peroxiredoxin 6 [78]	Enables system-wide target profiling
AI-Guided Target Prediction	Uses deep learning algorithms to predict NP-target interactions	Deep representation learning for multi-dimensional drug-target analysis [110]	High-throughput prediction with contextual biological networks

The integration of artificial intelligence and deep learning has significantly accelerated NP target identification, moving from "broad-spectrum screening" to "precise capture" [110]. These approaches combine ligand-based similarity methods with structural biology and systems-level network analysis to create multi-dimensional interaction maps for natural products.

Experimental Protocol: Affinity-Based Target Fishing for Natural Products

The following protocol outlines a standardized approach for identifying protein targets of natural products using affinity purification methodology [78] [110]:

• Probe Design and Synthesis:

  • Functionalize the natural product with a bioorthogonal handle (e.g., alkyne, azide) at a position that does not compromise bioactivity
  • Alternatively, immobilize the natural product to solid support (e.g., sepharose beads) via a flexible linker spacer

• Cell Lysate Preparation:

  • Culture relevant cell lines under appropriate conditions
  • Harvest cells and lyse using non-denaturing buffer (e.g., 50 mM Tris-HCl, 150 mM NaCl, 0.5% NP-40, pH 7.4) with protease inhibitors
  • Clarify lysate by centrifugation at 15,000 × g for 15 minutes at 4°C

• Affinity Purification:

  • Incubate cell lysate (1-2 mg total protein) with NP-conjugated beads (50-100 μL bed volume) for 2-4 hours at 4°C with gentle rotation
  • Wash beads extensively with lysis buffer (5 × 1 mL) to remove non-specifically bound proteins

• Target Elution and Identification:

  • Competitively elute bound proteins with excess free natural product (100-500 μM) or via denaturation
  • Subject eluted proteins to tryptic digestion and LC-MS/MS analysis
  • Validate putative targets through orthogonal methods (SPR, CETSA, functional assays)

This methodology has successfully identified numerous PPI targets for natural products, including celastrol's interaction with peroxiredoxins and HO-1, and withangulatin A's binding to peroxiredoxin 6 [78].

Diagram 1: Target fishing workflow for natural products using affinity purification.

Natural Product Payloads in Antibody-Drug Conjugates

Structural Classes and Mechanism of Action of NP-Derived ADC Payloads

Antibody-drug conjugates represent a paradigm-shifting approach to targeted cancer therapy, combining the specificity of monoclonal antibodies with the potent cytotoxicity of small molecules. Natural products have emerged as privileged scaffolds for ADC payloads due to their exceptional potency and evolved biological activity [108] [109].

Table 2: Natural Product-Derived Payloads in Approved Antibody-Drug Conjugates

Payload Class	Natural Product Origin	Molecular Target	Example ADC(s)	Potency (ICâ‚…â‚€)
Calicheamicins	Micromonospora echinospora	DNA minor groove	Gemtuzumab ozogamicin, Inotuzumab ozogamicin [108]	Low pM range
Auristatins	Dolastatin 10 (marine peptide)	Microtubules	Brentuximab vedotin, Polatuzumab vedotin [108] [109]	Sub-nM range
Maytansinoids	Maytansine (plant alkaloid)	Microtubules	Trastuzumab emtansine [108] [109]	Sub-nM range
Camptothecins	Camptothecin (plant alkaloid)	Topoisomerase I	Sacituzumab govitecan, Trastuzumab deruxtecan [109]	Low nM range
Amonatides	Amycolatopsis orientalis	DNA cross-linking	Loncastuximab tesirine [108]	Low pM range

The evolutionary optimization of natural products for biological system interaction makes them particularly suitable as ADC payloads. Their inherent membrane permeability, ability to engage multiple cell death pathways, and capacity to evade resistance mechanisms contribute to their exceptional performance in targeted delivery applications [105] [1].

ADC Mechanisms and Intracellular Processing

The therapeutic activity of natural product-based ADCs depends on a multi-step mechanism that begins with target recognition and concludes with payload-mediated cell death [109]:

• Antigen Binding and Internalization: The antibody component binds to tumor-associated antigens, resulting in receptor-mediated endocytosis of the ADC-antigen complex.

• Lysosomal Trafficking and Processing: Internalized ADCs traffic through endosomal compartments to lysosomes, where acidic pH and specific enzymes cleave the linker, releasing the active payload.

• Payload Mechanism of Action: Released natural product payloads engage their intracellular targets, with two primary mechanisms:

  • DNA-Targeting Agents: Calicheamicins and amonatides cause DNA double-strand breaks and cross-linking, triggering apoptosis
  • Microtubule-Targeting Agents: Auristatins and maytansinoids disrupt microtubule dynamics, arresting cell cycle progression

• Bystander Effect: Certain linker-payload combinations enable diffusion of the cytotoxic agent to neighboring cells, overcoming antigen heterogeneity

• Immunogenic Cell Death: Some NP payloads induce damage-associated molecular patterns (DAMPs), promoting antitumor immunity [1]

The efficiency of each step significantly influences overall ADC efficacy, with natural product properties contributing critically to the final cytotoxic stages.

Diagram 2: Mechanism of action of natural product-based antibody-drug conjugates.

Experimental Protocol: ADC Cytotoxicity and Bystander Effect Assessment

Evaluating the potency and bystander activity of natural product-based ADCs requires specialized in vitro protocols [109]:

• Target-Positive Cell Cytotoxicity Assay:

  • Seed antigen-positive tumor cells in 96-well plates at optimal density (e.g., 5,000 cells/well)
  • Treat with serial dilutions of ADC (typically 0.001-100 nM) for 72-120 hours
  • Assess viability using ATP-based (CellTiter-Glo) or metabolic (MTT) assays
  • Calculate ICâ‚…â‚€ values using four-parameter logistic regression

• Bystander Killing Assessment:

  • Establish co-cultures of antigen-positive and antigen-negative cells expressing different selection markers (e.g., GFP/RFP)
  • Treat co-cultures with ADC for 72-96 hours
  • Quantify viability of each population using flow cytometry or fluorescence imaging
  • Calculate bystander killing efficiency as percentage of antigen-negative cell death

• Mechanistic Validation:

  • For DNA-damaging payloads: Assess γH2AX foci formation via immunofluorescence
  • For microtubule-targeting payloads: Evaluate cell cycle arrest through propidium iodide staining
  • Confirm apoptosis induction via Annexin V staining and caspase activation assays

This comprehensive assessment strategy validates both the direct potency and potential activity against heterogeneous tumors, critical parameters for natural product-based ADC development.

Emerging Innovations and Future Perspectives

AI-Guided Design and Optimization

Artificial intelligence is revolutionizing both natural product discovery and ADC design through several key applications [111] [110]:

• De Novo Natural Product Design: Generative AI models trained on known NP structures can propose novel scaffolds with optimized properties for PPI modulation or ADC payload applications
• Target Prediction: Deep learning algorithms integrate chemical, genomic, and proteomic data to predict NP targets with increasing accuracy, accelerating mechanistic studies
• ADC Optimization: AI/ML models forecast optimal drug-to-antibody ratios, conjugation sites, and linker stability parameters, reducing experimental optimization cycles

The integration of AI with high-throughput experimental validation creates iterative design loops that significantly accelerate the development of NP-based therapeutics for challenging targets [111].

Novel Conjugation Platforms and Scaffold Diversity

Beyond conventional antibody platforms, emerging scaffold technologies offer new opportunities for natural product delivery [112]:

• Engineered Protein Scaffolds: DARPins, affibodies, and monobodies provide smaller, more stable targeting modules with tunable pharmacokinetics
• Genetically Encoded Systems: Fully genetically encoded fusion proteins combine targeting, linker, and cytotoxic natural product-derived domains in single polypeptides
• Nanoparticle-ADC Hybrids: Antibody-conjugated nanoparticles enable higher payload capacity and controlled release kinetics for natural products

These platforms address limitations of conventional ADCs, including structural heterogeneity, manufacturing complexity, and suboptimal tumor penetration, while leveraging the unique properties of natural product payloads [112] [111].

Sustainable Sourcing and Biosynthetic Engineering

The increasing demand for natural product-based therapeutics necessitates sustainable sourcing approaches [1]:

• Genome Mining and Synthetic Biology: Identification of biosynthetic gene clusters enables heterologous expression of complex NPs in tractable host organisms
• CRISPR-Cas Pathway Engineering: Precise genome editing optimizes NP production and enables structural diversification
• Plant Cell Fermentation and Tissue Culture: Provides controlled, scalable production of plant-derived NPs without agricultural constraints

These approaches ensure a sustainable supply of natural products while providing opportunities for structural diversification through pathway engineering.

The Scientist's Toolkit: Essential Research Reagents and Technologies

Table 3: Key Research Reagents and Platforms for Natural Product PPI and ADC Research

Reagent/Technology	Function	Application Context
Photoactivatable NP Probes	Covalent crosslinking to protein targets for identification	PPI target fishing [78]
SPR Biosensors	Quantify binding kinetics and affinity of NP-target interactions	Validation of PPI modulation [110]
Site-Specific Conjugation Systems	Generate homogeneous ADC constructs with defined DAR	ADC optimization [112] [109]
Tumor Organoid Co-cultures	Model tumor microenvironment and bystander effects	ADC efficacy assessment [109]
AntiSMASH Platform	Identify and analyze biosynthetic gene clusters	NP discovery and engineering [1]
DARPin Scaffold Libraries	Alternative targeting modules with enhanced stability	Next-generation ADC platforms [112]
CETSA Assay Kits	Monitor target engagement in cellular contexts	Validation of NP mechanism of action [78]

Natural products continue to play an indispensable role in addressing two of the most challenging areas in therapeutic development: protein-protein interaction modulation and targeted payload delivery through antibody-drug conjugates. Their evolutionary optimization for biological system interaction, structural complexity, and potent bioactivity position them uniquely for these applications. The convergence of advanced target identification technologies, innovative ADC platforms, and AI-guided design approaches is creating unprecedented opportunities to leverage natural product scaffolds against historically intractable targets. As chemical biology and systems-level research continue to advance, natural products will undoubtedly remain at the forefront of innovative therapeutic strategies for complex diseases, particularly in oncology. The integration of sustainable sourcing practices and biosynthetic engineering will ensure their continued relevance in the drug discovery ecosystem, bridging traditional knowledge with cutting-edge science to address unmet medical needs.

Conclusion

The integration of chemical biology with systematic principles is revitalizing natural product research, transforming it into a predictive and powerful discovery engine. The established correlation between taxonomic distance and chemical diversity provides a robust, data-driven strategy for bioprospecting, significantly increasing the odds of discovering novel scaffolds. Concurrently, technological convergence—spanning cell-free biosynthesis, AI-driven target prediction, and advanced metabolomics—is systematically overcoming historical bottlenecks of supply and characterization. Looking forward, this synergistic approach promises to unlock the vast, untapped potential of natural products, particularly for intractable therapeutic targets like protein-protein interactions and in the urgent fight against antimicrobial resistance. Future success will depend on continued interdisciplinary collaboration, further development of open-access databases, and the refined application of computational tools to navigate the complex yet rewarding chemobiological landscape.

References

• 1. Revitalizing natural products for sustainable drug Discovery [https://www.sciencedirect.com/science/article/abs/pii/S2352554125001809]
• 2. Innovative approaches in the discovery of terpenoid natural ... [https://www.sciencedirect.com/science/article/abs/pii/S1369527424001516]
• 3. Alkaloid [https://en.wikipedia.org/wiki/Alkaloid]
• 4. Alkaloids Biosynthesis - an overview [https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/alkaloids-biosynthesis]
• 5. Natural Composition and Biosynthetic Pathways of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9121007/]
• 6. Synthetic biology routes to new and extinct natural products [https://pubs.rsc.org/en/content/articlehtml/2025/cb/d5cb00047e]
• 7. Type II Polyketide Synthases: impact on human health ... [https://www.sciencedirect.com/science/article/pii/S0021925825026018]
• 8. The Structure-Activity Relationship of the Antioxidant ... [https://www.mdpi.com/1420-3049/21/1/72]
• 9. Structure-activity relationships of the antimicrobial peptide ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10768847/]
• 10. Novel Approach to Classify Plants Based on Metabolite-Content Similarity - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC5253511/]
• 11. Chemotaxonomy, an Efficient Tool for Medicinal Plant Identification: Current Trends and Limitations - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12299962/]
• 12. Natural product diversity and its role in chemical biology ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3110584/]
• 13. Deriving phylogenetic trees from the similarity analysis of ... [https://pubmed.ncbi.nlm.nih.gov/12855450/]
• 14. microbeMASST: a taxonomically informed mass ... [https://www.nature.com/articles/s41564-023-01575-9]
• 15. New insights in metabolism modelling to decipher plant ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12018784/]
• 16. Correlating chemical diversity with taxonomic distance for ... [https://www.nature.com/articles/s41467-018-03184-1]
• 17. Secondary metabolite profiling, growth profiles and other ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC2275202/]
• 18. The comparative analysis and identification of secondary ... [https://www.sciencedirect.com/science/article/pii/S2095311921636420]
• 19. Pangenome analysis reveals the genetic basis for ... [https://www.sciencedirect.com/science/article/pii/S0740002023001211]
• 20. Comprehensive prediction of secondary metabolite ... [https://www.nature.com/articles/s41467-020-19986-1]
• 21. Natural products in drug discovery: advances and ... [https://www.nature.com/articles/s41573-020-00114-z]
• 22. Phylogenetic Approaches to Natural Product Structure ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4504231/]
• 23. Phylogenetics - an overview [https://www.sciencedirect.com/topics/neuroscience/phylogenetics]
• 24. Phylogenetics [https://en.wikipedia.org/wiki/Phylogenetics]
• 25. Reconstructing Phylogeny by Aligning Multiple Metabolic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6017379/]
• 26. Phylophenetic properties of metabolic pathway topologies as ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-7-252]
• 27. The evolution of chemosystematics [https://pubmed.ncbi.nlm.nih.gov/17765936/]
• 28. (PDF) The evolution of chemosystematics [https://www.academia.edu/52350050/The_evolution_of_chemosystematics]
• 29. Chemotaxonomic Classification Applied to the Identification of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6151587/]
• 30. Exploring the known chemical space of the plant kingdom [https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00778-w]
• 31. Genome mining reveals the distribution of biosynthetic gene ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-025-11754-z]
• 32. Computational advances in biosynthetic gene cluster ... [https://www.sciencedirect.com/science/article/abs/pii/S0734975025000187]
• 33. Genome mining of tailoring enzymes from biosynthetic ... [https://www.sciencedirect.com/science/article/pii/S109671762500120X]
• 34. Genome mining based on transcriptional regulatory ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12161575/]
• 35. Cell-Free Synthetic Biology for Pathway Prototyping [https://www.sciencedirect.com/science/chapter/bookseries/abs/pii/S0076687918301800]
• 36. Cell-Free Systems: Evolving to Fill New Application Niches [https://www.synbiobeta.com/read/cell-free-systems-evolving-to-fill-new-application-niches]
• 37. Cell-Free Synthetic Biology | Frontiers Research Topic [https://www.frontiersin.org/research-topics/12103/cell-free-synthetic-biology/magazine]
• 38. A machine learning Automated Recommendation Tool for ... [https://www.nature.com/articles/s41467-020-18008-4]
• 39. MASS SPECTROMETRY-BASED METABOLOMICS - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC1904337/]
• 40. Mass spectrometry-based metabolomics approach in the ... [https://www.nature.com/articles/s41598-020-58046-y]
• 41. Guide to Metabolomics Analysis: A Bioinformatics Workflow [https://pmc.ncbi.nlm.nih.gov/articles/PMC9032224/]
• 42. MetaboAnalyst [https://www.metaboanalyst.ca/]
• 43. Natural Products Research—Information for Researchers - nccih [https://www.nccih.nih.gov/grants/nutritional-interventions-and-natural-products-research-information-for-researchers]
• 44. How AI drug discovery is reshaping biopharma [https://www.ddw-online.com/how-ai-drug-discovery-is-reshaping-biopharma-36402-202509/]
• 45. A survey on deep learning for drug-target binding prediction [https://pmc.ncbi.nlm.nih.gov/articles/PMC12451107/]
• 46. Leading AI-Driven Drug Discovery Platforms [https://www.sciencedirect.com/science/article/abs/pii/S0031699725075118]
• 47. Application of Artificial Intelligence In Drug-target ... [https://www.nature.com/articles/s44385-024-00003-9]
• 48. Progress of AI-Driven Drug–Target Interaction Prediction ... [https://www.mdpi.com/1422-0067/26/20/10037]
• 49. Natural Products and their application [https://www.sciencedirect.com/special-issue/10C9NNBJ91F]
• 50. Grand challenges in Chemical Biology from the ... [https://www.frontiersin.org/journals/chemical-biology/articles/10.3389/fchbi.2025.1731631/full]
• 51. Emerging paradigms in bioorthogonal chemistry for future ... [https://www.sciencedirect.com/science/article/abs/pii/S0378517325010737]
• 52. Recent advances in developing active targeting and multi- ... [https://www.nature.com/articles/s41392-022-01250-1]
• 53. Chemoenzymatic approaches to plant natural product ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9298680/]
• 54. Advancements in bioorthogonal chemistry and click ... [https://www.sciopen.com/article/10.26599/OSHM.2025.9610007]
• 55. Recent Advances in Silent Gene Cluster Activation ... [https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2021.632230/full]
• 56. A Natural Product Chemist's Guide to Unlocking Silent ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9148385/]
• 57. Recent Advances in Activating Silent Biosynthetic Gene ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6281788/]
• 58. Bridging the genotype–phenotype gap: what does it take? [https://pmc.ncbi.nlm.nih.gov/articles/PMC3634519/]
• 59. Global analysis of biosynthetic gene clusters reveals ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9177418/]
• 60. Bridging the Gap between Genotype and Phenotype via ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2012.00227/full]
• 61. Influences of chemotype and parental genotype on ... [https://www.nature.com/articles/s41598-023-38790-7]
• 62. Metabolic Engineering for the Production of Natural Products [https://pmc.ncbi.nlm.nih.gov/articles/PMC4077471/]
• 63. Cell-free synthetic biology for natural product biosynthesis ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11920963/]
• 64. Cell-free synthetic biology for in vitro biosynthesis of ... [https://www.sciencedirect.com/science/article/pii/S2405805X17301291]
• 65. Review Articles in 2025 | Nature Chemical Biology [https://www.nature.com/nchembio/articles?type=review-article&year=2025]
• 66. Natural product synthesis - Latest research and news [https://www.nature.com/subjects/natural-product-synthesis]
• 67. Designing pathways for bioproducing complex chemicals ... [https://www.nature.com/articles/s41467-025-59827-7]
• 68. Automated annotation of complex natural products using a ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12356265/]
• 69. Evaluation of Fourier deconvolution ion mobility ... [https://www.sciencedirect.com/science/article/abs/pii/S0021967323007859]
• 70. Hit Identification in Drug Discovery [https://www.vipergen.com/hit-identification-in-drug-discovery/]
• 71. A bottom-up approach to find lead compounds in ... [https://www.nature.com/articles/s42004-025-01610-2]
• 72. A systematic framework enhancing molecular screening ... [https://pubmed.ncbi.nlm.nih.gov/40582252/]
• 73. Target identification of natural medicine with chemical ... [https://www.nature.com/articles/s41392-020-0186-y]
• 74. Octacosanol: Biological activities and bioavailability ... [https://www.sciencedirect.com/science/article/abs/pii/S0308814625041433]
• 75. Novel Strategies for the Bioavailability Augmentation and ... [https://pubmed.ncbi.nlm.nih.gov/36612264/]
• 76. Enhancing bioavailability of natural extracts for nutritional ... [https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2023.1190912/full]
• 77. Labeled and Label-Free Target Identifications of Natural ... [https://pubmed.ncbi.nlm.nih.gov/39360958/]
• 78. Recent advances in target identification technology of ... [https://www.sciencedirect.com/science/article/abs/pii/S0163725825000452]
• 79. Natural Dietary Compounds in the Treatment of Arsenic Toxicity [https://pmc.ncbi.nlm.nih.gov/articles/PMC9370003/]
• 80. The LOTUS initiative for open knowledge management in ... [https://elifesciences.org/articles/70780]
• 81. Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC10972680/]
• 82. ChEMBL [https://www.ebi.ac.uk/chembl/]
• 83. ChEMBL 36 is live [https://www.embl.org/news/updates-from-data-resources/chembl-36/]
• 84. Data extraction and comparison for complex systematic reviews: a step-by-step guideline and an implementation example using open-source software - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC10691069/]
• 85. Myxobacteria as a Source of New Bioactive Compounds [https://pmc.ncbi.nlm.nih.gov/articles/PMC8401837/]
• 86. Chromosomal organization of biosynthetic gene clusters ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1227206/full]
• 87. Survey of Biosynthetic Gene Clusters from Sequenced ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6616573/]
• 88. Establishing Conserved Biosynthetic Gene Clusters of the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12224551/]
• 89. Genetic engineering of Sorangium cellulosum reveals ... [https://www.nature.com/articles/s41467-025-63441-y]
• 90. South African Myxococcota: an untapped resource for ... [https://link.springer.com/article/10.1007/s00253-025-13586-z]
• 91. An Assessment of Chemical Diversity in Microbial Natural ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12464764/]
• 92. A Historical Overview of Natural Products in Drug Discovery [https://pmc.ncbi.nlm.nih.gov/articles/PMC3901206/]
• 93. Mechanism, Efficacy, and Safety of Natural Antibiotics - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12561696/]
• 94. Natural products chemistry: The emerging trends and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6036106/]
• 95. Combating bacterial antibiotic resistance with ... [https://www.sciencedirect.com/science/article/pii/S2590098625000259]
• 96. A systematic review on natural products with antimicrobial ... [https://eurjmedres.biomedcentral.com/articles/10.1186/s40001-025-02717-x]
• 97. Anticancer Drug Discovery Based on Natural Products [https://pmc.ncbi.nlm.nih.gov/articles/PMC12346478/]
• 98. Frontiers in Natural Product-based Cancer Researches [https://communities.springernature.com/posts/frontiers-in-natural-product-based-cancer-research-from-natural-products-to-drug-candidates]
• 99. The parallel and combinatorial synthesis and screening in ... [https://link.springer.com/article/10.1007/s11224-025-02595-3]
• 100. Time-Dependent Comparison of the Structural Variations ... [https://www.mdpi.com/1422-0067/25/21/11475]
• 101. CoLiNN: A Tool for Fast Chemical Space Visualization of ... [https://pubmed.ncbi.nlm.nih.gov/40099935/]
• 102. Applications of Natural and Pseudo-Natural Products in ... [https://www.mdpi.com/journal/cimb/special_issues/1V6D29J00P]
• 103. New catalysis method can generate a library of novel ... [https://news.ucsb.edu/2025/022011/new-catalysis-method-can-generate-library-novel-molecules-drug-discovery]
• 104. Retrospective analysis of natural products provides insights ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5465889/]
• 105. The latest advances with natural products in drug discovery ... [https://pubmed.ncbi.nlm.nih.gov/40391763/]
• 106. Qualitative and Quantitative Analysis of Bioactive Natural ... [https://www.mdpi.com/journal/molecules/topical_collections/TC_Qualitative_Quantitative]
• 107. Recent developments in qualitative and quantitative ... [https://www.sciencedirect.com/science/article/abs/pii/S0731708512004931]
• 108. The next frontier in antibody-drug conjugates - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC12366496/]
• 109. Antibody–drug conjugates in cancer therapy [https://molecular-cancer.biomedcentral.com/articles/10.1186/s12943-025-02489-2]
• 110. Target fishing: from “needle in haystack” to “precise ... [https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1673688/full]
• 111. AI-driven innovation in antibody-drug conjugate design [https://www.frontiersin.org/journals/drug-discovery/articles/10.3389/fddsv.2025.1628789/full]
• 112. Toward a New Era of Programmable Therapeutic Proteins [https://www.sciencedirect.com/science/article/abs/pii/S0006291X25017346]