Revolutionizing Natural Product Discovery: A Comprehensive Guide to Computer-Assisted Structure Elucidation (CASE)

Abstract

This article provides an in-depth exploration of Computer-Assisted Structure Elucidation (CASE) systems and their transformative role in natural product research and drug development. Tailored for researchers, scientists, and drug development professionals, it covers the foundational evolution of CASE from 1D to 2D NMR-based systems[citation:1], details modern methodological workflows that integrate spectral data for de novo structure generation[citation:2][citation:4], addresses common troubleshooting scenarios and optimization strategies for complex molecules[citation:1][citation:3], and evaluates validation protocols and comparative performance of leading CASE platforms[citation:3][citation:9]. The synthesis of these four core intents highlights how CASE enhances accuracy, accelerates discovery, and reduces structural misassignments in biomedical research.

The Evolution and Core Principles of CASE in Natural Product Research

The structure elucidation of complex natural products represents a fundamental challenge in drug discovery and chemical research. For decades, Nuclear Magnetic Resonance (NMR) spectroscopy has served as the primary analytical tool for this task [1]. The historical trajectory from one-dimensional (1D) to two-dimensional (2D) NMR experiments marked a revolutionary advance, providing the detailed through-bond and through-space correlations necessary to unravel complex molecular architectures [2]. In parallel, the field of Computer-Assisted Structure Elucidation (CASE) evolved from theoretical concepts in the late 1960s to sophisticated expert systems that now integrate these multidimensional NMR datasets [3] [4]. Within the context of a broader thesis on CASE for natural products, this article details the critical pathway of this technological synergy. It outlines the key application notes, provides definitive experimental protocols for cornerstone 2D NMR experiments, and demonstrates how modern expert systems leverage this data to solve structures with unprecedented speed and accuracy, minimizing the risk of erroneous assignments that persist in the literature [1] [5].

Historical Trajectory: From Fundamental Discovery to 2D NMR and CASE

The development of NMR and CASE is a story of incremental foundational discoveries followed by transformative technological leaps. The initial observation of nuclear magnetic resonance in bulk materials was independently reported by Felix Bloch and Edward Purcell in 1946 [6]. The subsequent award of the Nobel Prize in Physics in 1952 confirmed the technique's significance. Early applications in chemistry relied on 1D spectra of nuclei like ¹H and ¹³C [7] [6]. While providing critical information on chemical environment, 1D NMR lacked the power to directly trace atomic connectivity in complex molecules, creating a bottleneck for structure elucidation [4].

The conceptual breakthrough for 2D NMR came from Jean Jeener in 1971, with the first practical implementation of Correlation Spectroscopy (COSY) by Aue, Bartholdi, and Ernst in 1976 [2]. This introduced a second frequency dimension, spreading out spectral data and allowing the correlation of coupled nuclei. The subsequent development of experiments like TOCSY, HSQC, and HMBC provided a comprehensive toolkit for establishing molecular connectivities [2]. The routine availability of these 2D experiments in the 1990s provided the rich, interconnected datasets required to make CASE a practical reality [3] [8].

Simultaneously, the field of CASE was born in 1968 with early systems attempting to use infrared and 1D NMR data [3] [4]. These initial systems were limited to small, simple molecules. The pivotal moment arrived when CASE algorithms were redesigned to ingest and process the correlation data from 2D NMR experiments [9] [4]. This integration enabled expert systems to solve increasingly complex problems, culminating in modern software capable of outperforming human experts in speed and often in reliability for planar structure determination [9] [3].

Table 1: Key Historical Milestones in NMR and CASE Development

Year	Milestone	Key Contributors/Systems	Significance
1946	First detection of NMR in bulk materials	Bloch; Purcell, Torrey, Pound [6]	Foundation of NMR spectroscopy.
1952	Nobel Prize in Physics for NMR discovery	Felix Bloch, Edward Purcell [6]	Recognition of NMR's fundamental importance.
1968	First publications on CASE methods	Elyashberg, Gribov et al. [3]	Birth of computer-assisted structure elucidation.
1971 / 1976	Concept and first implementation of 2D NMR (COSY)	Jeener; Aue, Bartholdi, Ernst [2]	Introduction of a second spectral dimension, enabling correlation spectroscopy.
1990s	2D NMR becomes routine; CASE systems begin using 2D data	- [3] [4] [8]	Provides the necessary data density for effective CASE application to complex molecules.
1997	Development of ACD/Structure Elucidator begins	Mikhail Elyashberg & ACD/Labs [9] [3]	Creation of a leading commercial CASE expert system.
2003	First report of a CASE system outperforming human experts	ACD/Structure Elucidator [3]	Demonstrated the practical power of automated elucidation.
2020s	CASE systems solve complex natural products with 100% success in challenges	Modern CASE programs (e.g., ACD/SE) [9] [3]	Maturation of expert systems as reliable, high-throughput tools for research.

Core 2D NMR Experiments for Structure Elucidation: Protocols and Applications

Modern structure elucidation of natural products relies on a suite of complementary 2D NMR experiments. Each provides specific pieces of the structural puzzle, from direct bond connections to long-range relationships and spatial proximity.

¹H-¹H Correlation Spectroscopy (COSY)

Application Note: The COSY experiment identifies nuclei that are coupled through a small number of bonds (typically 2-3 bonds for vicinal protons) [2]. It is the primary tool for establishing the proton-proton connectivity network within a spin system, such as along a carbon chain or around a ring system.

Detailed Protocol:

• Sample Preparation: Dissolve 2-10 mg of the natural product in 0.6 mL of a deuterated solvent (e.g., CDCl₃, DMSO-d₆). Filter the solution into a standard 5 mm NMR tube to remove particulates.
• Instrument Setup: Lock, tune, and shim the spectrometer on the sample. Set the probe temperature (typically 25°C). Calibrate the 90° pulse width for protons.
• Parameter Definition:
  • Spectral Width: Adjust to encompass the entire ¹H spectral region (e.g., 0-12 ppm).
  • Pulse Sequence: Select the standard COSY-90 or phase-sensitive double-quantum filtered (DQF-COSY) sequence. DQF-COSY is preferred as it produces cleaner spectra with reduced diagonal intensity [2].
  • Data Points: Set t₂ (acquisition) = 2K points; t₁ (incremented dimension) = 512 increments.
  • Scans per Increment: 4-16, depending on sample concentration.
  • Relaxation Delay: 1.0-1.5 seconds.
• Data Acquisition: Run the experiment. The sequence applies a 90°-t₁-90°-Acquire scheme, incrementing t₁ for each FID [2].
• Processing:
  • Apply an appropriate window function (e.g., sine-bell or qsine) in both dimensions.
  • Perform a double Fourier transformation.
  • Phase correct the spectrum to pure absorption mode in both dimensions.
  • Adjust the baseline.
• Interpretation: Identify cross-peaks symmetrically displaced from the diagonal. Each cross-peak indicates scalar coupling between the protons at the corresponding chemical shifts on the F1 and F2 axes [2].

Heteronuclear Single Quantum Coherence (HSQC)

Application Note: The HSQC experiment correlates directly bonded protons and heteronuclei (e.g., ¹H to ¹³C). It provides a critical "map" assigning each proton to its directly attached carbon atom, differentiating CH, CH₂, and CH₃ groups.

Detailed Protocol:

• Sample & Setup: Use the same sample as for COSY. Tune the probe to both ¹H and ¹³C channels.
• Parameter Definition:
  • Spectral Widths: F2 (¹H): 0-12 ppm; F1 (¹³C): 0-220 ppm (aliphatic/aromatic region).
  • Pulse Sequence: Select an inverse-detection HSQC sequence (sensitivity-enhanced or gradient-selected).
  • Data Points: Set t₂ = 2K points; t₁ = 256 increments.
  • Scans per Increment: 8-32 (due to lower ¹³C sensitivity).
  • ¹JCH Coupling Constant: Set to ~145 Hz as the evolution parameter.
• Data Acquisition & Processing: Execute the sequence. Process with window functions, Fourier transform in both dimensions, phase correct, and calibrate.

Heteronuclear Multiple Bond Correlation (HMBC)

Application Note: HMBC is arguably the most powerful experiment for skeletal assembly. It detects correlations between protons and carbons (or other heteronuclei) separated by 2-3 bonds (²,³JCH), and sometimes 4 bonds [9] [4]. This "long-range" connectivity links molecular fragments together through quaternary centers.

Detailed Protocol:

• Sample & Setup: Identical to HSQC setup.
• Parameter Definition:
  • Spectral Widths: As for HSQC.
  • Pulse Sequence: Select a gradient-selected HMBC sequence.
  • Data Points: t₂ = 2K; t₁ = 200 increments.
  • Scans per Increment: 16-64.
  • Long-Range Coupling Constant (ⁿJCH): Optimize for ~8 Hz. An "accordion" version can vary this delay to detect a range of couplings [4].
  • Low-Pass J-Filter: Set to suppress one-bond correlations (e.g., using a ~145 Hz delay).
• Data Acquisition & Processing: Run and process similarly to HSQC, typically in magnitude mode.
• Interpretation Caution: Correlations from couplings over >3 bonds ("non-standard correlations" or NSCs) are common and can lead to misinterpretation if not accounted for. Modern CASE systems have specific algorithms to handle these [9] [4].

Table 2: Key 2D NMR Experiments for Natural Product Structure Elucidation

Experiment	Correlation Type	Typical Range (Bonds)	Primary Application in Structure Elucidation	Key Limitation/Caution
COSY	¹H ¹H (homonuclear)	2-3 (vicinal) [2]	Establishes proton connectivity within spin systems.	Cannot link protons across quaternary centers or large coupling distances.
TOCSY	¹H ¹H (homonuclear)	Entire spin system [2]	Identifies all protons within an isolated coupled network (e.g., a single sugar residue).	Mixing time determines extent of correlation; can be complex in overlapping systems.
HSQC	¹H ¹³C (heteronuclear, 1-bond)	1 (direct bond)	Assigns protons to their directly attached carbons; identifies CHₙ multiplicity.	Does not provide connectivity information between fragments.
HMBC	¹H ¹³C (heteronuclear, long-range)	2-3 (²,³JCH), sometimes 4 [9]	Most critical for skeleton assembly. Connects structural fragments via quaternary carbons and heteroatoms.	Presence of non-standard correlations (>3 bonds) can cause ambiguity if not handled properly [4].

Diagram Title: Integrated CASE Workflow from 2D NMR Data to Structure

The Modern 2D NMR Expert System: Architecture and Application

The integration of 2D NMR data into CASE systems transformed them from simple fragment assemblers into powerful expert systems. A leading example, ACD/Structure Elucidator (ACD/SE), exemplifies this architecture [9].

System Architecture and Workflow:

• Data Input and Processing: The system imports raw or processed 1D and 2D NMR data. Integrated software (e.g., ACD/SpecManager) extracts chemical shifts, multiplicities, and, critically, peak lists from 2D spectra (COSY, HSQC, HMBC) to generate tables of connectivities [9].
• Molecular Connectivity Diagram (MCD): This is the core internal representation. Atoms (C, H, N, O, etc.) from the molecular formula are displayed with their chemical shifts. Extracted connectivities are drawn as colored lines: blue for COSY (2-3 bond H-H) and green for HMBC (typically 2-3 bond H-C) [9]. The user can review and edit this diagram.
• Logical Analysis and Contradiction Checking: Before structure generation, the system performs a critical logical analysis of the MCD to check for inconsistencies, such as impossible atom valences or conflicting constraints. It also automatically flags potential Non-Standard Correlations (NSCs)—HMBC peaks suggesting connections longer than 3 bonds—which are a major source of error in manual interpretation [9] [4].
• Structure Generation: A structure generator assembles all possible planar structures that satisfy the molecular formula and the connectivity constraints in the MCD. To handle NSCs, the system uses "fuzzy structure generation," relaxing bond distance constraints for flagged atoms to ensure the correct structure is within the generated set [4].
• Ranking and Selection: The final candidate structures are ranked by comparing their predicted NMR spectra (using integrated empirical or, increasingly, DFT-based predictors) against the experimental data [9] [8]. The structure with the best statistical match is proposed as the solution. This process can generate and evaluate millions of candidates in minutes [3].

Diagram Title: Evolution from 1D NMR to 2D NMR Expert Systems

The Scientist's Toolkit: Essential Reagents and Materials

Table 3: Essential Research Reagent Solutions for 2D NMR-Based Structure Elucidation

Item	Specification / Example	Function in Protocol
Deuterated NMR Solvents	CDCl₃, DMSO-d₆, CD₃OD, D₂O	Provides a signal for the spectrometer lock system and dissolves the sample without adding interfering proton signals.
NMR Reference Standard	Tetramethylsilane (TMS) or solvent residual peak (e.g., CHCl₃ at 7.26 ppm in CDCl₃)	Provides a reference point (0 ppm) for calibrating the chemical shift scale of the spectrum.
High-Purity Natural Product Sample	2-10 mg, purified via HPLC/LCMS	The analyte of interest. Purity is critical to avoid overlapping signals from impurities.
Shigemi or Standard NMR Tube	5 mm outer diameter, matched for the spectrometer	Holds the sample in the magnetic field. Shigemi tubes allow for smaller sample volumes.
CASE Expert System Software	ACD/Structure Elucidator, or similar [9] [3]	The software platform that automates data interpretation, structure generation, and ranking.
NMR Prediction Software	Integrated in CASE or standalone (e.g., ACD/NMR Predictors, DFT software)	Calculates expected NMR spectra for candidate structures to enable ranking and verification [9] [8].

The historical development from 1D NMR beginnings to modern 2D NMR expert systems has fundamentally reshaped natural products research. CASE systems, built upon the rich correlation data from experiments like HMBC and COSY, have matured into indispensable tools that provide unbiased, thorough, and rapid structural hypotheses [3] [8]. They systematically address the challenge of non-standard correlations and can now reliably elucidate structures that have stymied experts for years [9] [4].

Future advancements point toward even greater integration and automation. The incorporation of more sophisticated quantum mechanical calculations, such as Density Functional Theory (DFT), for both chemical shift prediction and 3D conformation analysis is ongoing [1] [8]. Research into quantum sensing using defects in 2D materials like hexagonal boron nitride promises the potential for NMR detection at the single-molecule level, which could revolutionize sensitivity [10]. Furthermore, the integration of CASE systems directly with spectroscopic hardware for automated, on-the-fly analysis represents the next frontier in high-throughput structure elucidation [4]. As these tools become more accessible and user-friendly, their application is expected to become standard practice, ensuring greater accuracy and efficiency in discovering and characterizing the complex molecules that serve as the foundation for new therapeutics [8].

The discovery of novel bioactive natural products (NPs) is systematically hindered by two major bottlenecks: the re-isolation of known compounds and the arduous process of solving novel chemical structures [11]. Within the framework of a broader thesis on Computer-Assisted Structure Elucidation (CASE), this article defines and delineates three critical, interdependent processes: dereplication, structure verification, and de novo structure elucidation. These processes represent points on a continuum of analytical certainty, from identification to full structural discovery [12].

Dereplication acts as the essential first filter, aiming to rapidly identify known compounds in a mixture to prioritize novel leads [11] [13]. Structure verification serves as a confirmatory checkpoint, typically using spectroscopic data to validate that an isolated compound matches a hypothesized or expected structure [14] [12]. De novo elucidation is the most complex undertaking, involving the complete and unbiased determination of an unknown chemical structure from analytical data alone [15] [12]. Modern CASE systems synergize data from multiple spectroscopic techniques—primarily Nuclear Magnetic Resonance (NMR) and high-resolution tandem Mass Spectrometry (HR-MS/MS)—with computational algorithms and database mining to accelerate and objectify each of these processes [14] [15] [11]. This integration is transforming NP research from a serendipitous, one-off process into a high-throughput, predictive discovery pipeline [16] [17].

Defining the Methodological Spectrum

The successful application of CASE requires a clear understanding of the distinct inputs, objectives, and outputs for dereplication, verification, and elucidation. The following table summarizes their defining characteristics and roles within the NP discovery workflow.

Table 1: Core Characteristics of Dereplication, Verification, and De Novo Elucidation

Aspect	Dereplication	Structure Verification	De Novo Structure Elucidation
Primary Goal	Early, rapid identification of known compounds to avoid redundancy.	Confirm or refute a proposed chemical structure.	Determine the complete, unknown chemical structure from analytical data.
Typical Input	Crude or partially purified extract; HR-MS/MS data; sometimes 1D NMR.	Purified compound; a proposed candidate structure; full suite of 1D/2D NMR and MS data.	Purified compound of unknown structure; comprehensive 1D/2D NMR and HR-MS/MS data.
Key Analytical Tools	LC-HRMS, MS/MS, molecular networking, database search (e.g., GNPS).	NMR prediction & comparison, MS fragmentation matching, automated verification (ASV) software.	CASE expert systems (e.g., ACD/Structure Elucidator), DFT-NMR calculations, SIRIUS.
Data Interpretation	Comparative: Searches against spectral or structural libraries.	Confirmatory: Assesses consistency between data and a single candidate.	Generative: Uses data as constraints to mathematically enumerate all possible structures.
Output	Identity of known compound or close analogue; "novelty flag."	Binary result (Confirmed/Rejected) with confidence scoring; may suggest alternatives.	One or more candidate structures ranked by probability; often a single definitive solution.
Role in NP Workflow	Priority filter at the extract stage.	Quality control check after isolation or synthesis.	Discovery engine for novel chemical entities.

Core Workflows and Performance Metrics

The operationalization of these concepts relies on specific computational workflows. Their effectiveness is benchmarked using quantitative metrics, as summarized below.

Table 2: Performance Metrics of Key CASE and Dereplication Tools

Tool/Method	Type	Key Metric	Reported Performance	Application Context
SIRIUS 4 [18]	De Novo MS Annotation	Correct Identification Rate	>70% when searching fragmentation spectra in a structure database.	Molecular formula annotation and structure prediction from HR-MS/MS data.
DEREPLICATOR [16]	Peptidic NP Dereplication	False Discovery Rate (FDR)	7.3% FDR at peptide level (p-value threshold 10⁻¹⁰) in GNPS spectra search.	High-throughput identification of known PNPs and their variants from MS/MS data.
MSⁿ Spectral Trees [19]	Spectral Library Matching	Library Size & Validation	872 MSⁿ spectra from 549 metabolites; validated with 765 replicate spectra.	Metabolite identification via automated comparison of multistage mass spectral trees.
CASE + DFT [15]	De Novo Elucidation/Verification	Problem-Solving Capability	Successfully resolved challenging NPs (aquatolide, coniothyrione) where empirical prediction failed.	Definitive structure verification and revision of complex stereochemistry.
Structure-Based Screening [20]	Virtual Dereplication	Library Screening Scale	Screened 26,311 NP structures against SARS-CoV-2 targets using 60% similarity cut-off.	Identifying NPs with structural and pharmacological similarity to known drugs.

Workflow Overview: The hierarchical relationship between these processes can be visualized as a decision tree that guides researchers from initial analysis to final confirmation.

Detailed Experimental Protocols

Protocol 1: High-Throughput Dereplication of Microbial Extracts Using LC-HRMS/MS and Molecular Networking

Objective: To rapidly identify known metabolites in crude fermentation broths or microbial colony extracts [13]. Materials: LC-HRMS system (e.g., Q-TOF or Orbitrap); Global Natural Products Social Molecular Networking (GNPS) platform; SIRIUS 4 software [11] [18].

• Sample Preparation & Analysis:
  • Prepare a methanolic extract of microbial culture. For direct colony screening, use ambient ionization techniques like DESI or nanoDESI [13].
  • Acquire LC-HRMS/MS data in data-dependent acquisition (DDA) mode. Use a reversed-phase column with a water/acetonitrile gradient. Collect high-resolution full-scan MS (resolution > 60,000) and MS/MS spectra for top ions.
• Data Processing:
  • Convert raw files to open formats (.mzML). Perform feature detection (peak picking, deisotoping, alignment) using tools like MZmine or MS-DIAL.
  • Export the consensus MS/MS spectrum list (with m/z, retention time, and fragmentation patterns) for GNPS analysis.
• Molecular Networking & Database Search:
  • Upload data to the GNPS workflow. Create a molecular network using the spectral clustering function (cosine score > 0.7) [11].
  • Annotate network nodes by searching against GNPS spectral libraries (e.g., MassBank, ReSpect) and structural databases.
  • For specialized dereplication (e.g., peptidic NPs), use DEREPLICATOR tool within GNPS, which searches against databases like AntiMarin and evaluates statistical significance (p-value, FDR) [16].
• De Novo Annotation for Novel Features:
  • For nodes not matching known spectra, use in-silico tools. Submit the MS/MS data to SIRIUS 4 for molecular formula prediction and subsequent structure fingerprint prediction [18].
  • This step may suggest a structural class or partial structure, guiding isolation decisions.

Protocol 2: Automated Structure Verification (ASV) by NMR

Objective: To objectively confirm the identity of a purified compound against a proposed structure without analytical bias [14] [12]. Materials: Pure compound (>1 mg); NMR spectrometer (≥400 MHz); ASV software (e.g., ACD/Labs ASV, MestReNova).

• Data Acquisition:
  • Dissolve the compound in an appropriate deuterated solvent. Acquire a standard NMR dataset: ¹H, ¹³C, HSQC, and HMBC spectra. COSY and NOESY/ROESY are recommended for full confidence [12].
• Software Processing & Input:
  • Process all spectra (phase, baseline correct, reference). Import the processed spectra and the proposed chemical structure (as a MOL or SDF file) into the ASV software.
• Automated Verification Run:
  • The software will automatically assign all peaks in the spectra to the proposed structure.
  • It will calculate a theoretical NMR spectrum for the proposed structure and compare it to the experimental data, generating a numerical match factor (e.g., NMR Match Factor) and a visual overlay (mirror plot).
  • The ASV algorithm provides a "pass/fail" result. In case of a marginal pass or fail, the software may highlight inconsistent peaks and suggest a list of alternative candidate structures that better fit the data [14].
• Expert Review:
  • All results, especially flagged ones, must be reviewed by an expert. The software's suggestions serve as an unbiased check against human cognitive bias [14].

Protocol 3: De Novo Structure Elucidation via CASE System with DFT Validation

Objective: To determine the complete planar and stereochemical structure of an unknown natural product [15]. Materials: Purified unknown compound (2-5 mg); NMR spectrometer (≥500 MHz recommended); CASE software (e.g., ACD/Structure Elucidator); DFT computation software (e.g., Gaussian).

• Comprehensive NMR Data Collection:
  • Acquire a complete set of 1D and 2D NMR spectra: ¹H, ¹³C, DEPT, COSY, TOCSY, HSQC, HMBC, and NOESY/ROESY. Ensure excellent spectral quality and resolution.
• Data Preprocessing and CASE Input:
  • Pick all relevant peaks in the 2D spectra. Input the peak lists (chemical shifts and correlations) along with the molecular formula (determined by HR-MS) into the CASE system.
• Structure Generation and Ranking:
  • The CASE system uses the spectral constraints to generate a Molecular Connectivity Diagram (MCD) and then exhaustively enumerates all possible constitutional isomers [15].
  • The initial list of structures is filtered and ranked using empirical chemical shift prediction algorithms (e.g., HOSE codes, neural networks).
• DFT-Based Verification and Final Selection:
  • For the top 2-5 candidate structures, perform geometry optimization and NMR chemical shift calculation using DFT methods (e.g., mPW1PW91/6-31+G(d,p) level).
  • Compare the computed chemical shifts (¹³C and ¹H) with the experimental data using statistical measures (e.g., DP4 probability, mean absolute error) [15].
  • The candidate with the highest statistical agreement is selected as the correct structure. This synergistic approach is critical for resolving complex or previously misassigned structures [15].

Integrated CASE Workflow: The synergy of spectroscopic data, CASE logic, and quantum mechanical calculations forms a powerful pipeline for definitive elucidation.

The Scientist's Toolkit: Essential Research Reagents & Software

Table 3: Key Software and Database Tools for CASE Workflows

Tool Name	Category	Primary Function	Application Scope
ACD/Structure Elucidator [14] [15]	CASE Expert System	De novo structure generation from NMR/MS constraints.	Core engine for solving unknown structures.
GNPS & DEREPLICATOR [16] [11]	Cloud Platform & Algorithm	Mass spectral networking, library search, and PNP dereplication.	High-throughput dereplication and analog discovery.
SIRIUS 4 [18]	MS Data Analysis	Molecular formula identification and fragmentation tree analysis.	De novo annotation when no library match exists.
Mestrelab Mnova	NMR/MS Processing	NMR data analysis, verification (ASV), and reporting.	Unified platform for processing and verifying spectroscopic data.
AntiMarin Database [16]	Chemical Database	Curated database of marine natural products and PNPs.	Target database for specialized dereplication searches.
Gaussian	Quantum Chemistry	DFT calculations for NMR chemical shift and energy prediction.	Definitive stereochemical assignment and CASE validation.
Osiris DataWarrior [20]	Chemoinformatics	Property calculation, structural similarity screening, and filtering.	Pre-filtering NP libraries based on properties and similarity.

The structural elucidation of complex natural products (NPs) represents a formidable challenge in drug discovery and chemical research. These molecules possess unparalleled architectural diversity and stereochemical complexity, which are key drivers of their potent and selective bioactivities. However, these same features render their definitive structural characterization using traditional methods prone to error and inefficiency [1] [21]. This document frames these challenges within the transformative context of Computer-Assisted Structure Elucidation (CASE), a paradigm that is fundamentally reshaping NP research. Modern CASE systems act as expert partners, integrating spectroscopic data—primarily 2D NMR correlations—with computational chemistry and database knowledge to generate and rank all plausible structural hypotheses [1] [22]. This shift from manual interpretation to data-driven, algorithmic analysis is mitigating the high rate of incorrect structure assignments historically reported in the literature and is accelerating the transition from crude extract to validated molecular structure [1] [23]. The protocols herein detail the application of an integrated CASE workflow, designed to harness key technological drivers—advanced NMR experiments, high-resolution mass spectrometry (HRMS), and predictive computational models—to systematically decode NP complexity.

Core Principles of CASE for Natural Products

CASE systems operate on the core principle of systematic structure generation constrained by empirical spectroscopic data. The primary inputs are atom connectivity rules derived from 2D NMR experiments, particularly ^1H-^1H COSY (homonuclear correlation) and ^1H-^13C HMBC (heteronuclear multiple-bond correlation). A foundational, though sometimes limiting, assumption is that all observed HMBC correlations correspond to ^2J_CH or ^3J_CH couplings (2- or 3-bond relationships) [1]. The system uses these constraints to assemble molecular connectivity lists, from which it generates all possible planar chemical structures that do not violate the input data. These candidate structures are then ranked using probabilistic methods or by comparing predicted NMR chemical shifts (calculated via empirical or Density Functional Theory (DFT) methods) with the experimental dataset [22]. The output is not a single answer but a ranked list of plausible structures, providing a quantifiable measure of confidence and highlighting potential ambiguities for further experimental investigation. This process directly addresses NP complexity by exhaustively exploring chemical space defined by the data, reducing human bias and oversight [1].

Table 1: Comparison of Representative CASE Systems and Their Methodological Focus

System / Approach	Primary Data Inputs	Core Algorithmic Method	Key Strength	Reported Limitation / Challenge
Structure Elucidator (ACD/Labs)	1D/2D NMR (COSY, HSQC, HMBC), MS, Molecular Formula [22]	Deterministic Structure Generator + NMR Prediction & Ranking	Robust handling of complex, proton-deficient molecules; extensive database integration.	Performance can degrade with violations of the 2-/3-bond HMBC assumption or very high molecular symmetry [1].
Cologne University System	2D NMR Correlations (COSY, HMBC) [1]	Fragmentation & Reassembly Logic	Effective at identifying structural fragments from correlation data prior to full assembly.	Requires careful user validation of generated fragments; less automated than full black-box systems.
Stochastic CASE Programs	NMR correlations, optional MS & IR data [23]	Stochastic (Monte Carlo) Search Algorithms	Can escape local minima in structure space; useful for novel scaffolds with few database analogues.	Computationally intensive; may require more user intervention to guide the search.
DFT-NMR Integrated Workflow	2D NMR Data + High-Level DFT Calculations (e.g., GIAO) [1] [22]	CASE Generation followed by DFT-NMR Shift Prediction & DP4 Analysis	Provides extremely high confidence in stereochemistry and structural validation; gold standard for complex cases.	Computationally expensive (days of CPU time); requires expertise in computational chemistry setup.
AI/ML-Enhanced Prediction	Broad spectral datasets, structural databases [24]	Machine Learning (e.g., Neural Networks) for shift prediction or substructure recognition	Potential for rapid, direct prediction from raw or pre-processed spectral data.	Currently emerging; dependent on quality, size, and diversity of training datasets [25] [24].

Application Notes & Detailed Protocols

Protocol 1: Integrated MS/NMR Data Acquisition for CASE Input

Objective: To prepare a comprehensive and high-quality spectroscopic dataset suitable for reliable CASE analysis.

Materials:

• Purified natural product sample (>0.5 mg, ideally >90% purity by NMR).
• Deuterated NMR solvent (e.g., CD_3OD, DMSO-d_6).
• High-resolution mass spectrometer (HRMS) with ESI or MALDI source.
• NMR spectrometer (≥ 400 MHz for ^1H frequency recommended).

Procedure:

• High-Resolution Mass Spectrometry (HRMS):
  • Dissolve a small aliquot (~10-50 µg) of the sample in a suitable volatile solvent (e.g., MeOH, CH_3CN).
  • Acquire HRMS data in both positive and negative ionization modes to determine the molecular ion ([M+H]^+, [M+Na]^+, [M-H]^-).
  • Critical Parameter: Achieve mass accuracy < 3 ppm. Use this data to establish the molecular formula (e.g., C_30H_48O_4). This formula is a mandatory constraint for the CASE system, dramatically reducing the combinatorial space for structure generation [25].

• NMR Sample Preparation & 1D Acquisition:

  • Dissolve the majority of the sample in 0.6 mL of deuterated solvent. Transfer to a standard 5 mm NMR tube.
  • Acquire standard ^1H NMR and ^13C NMR (or edited HSQC for ^13C chemical shifts) spectra.
  • Data Quality Note: Ensure the ^1H spectrum has a high signal-to-noise ratio (SNR > 50:1 for key resonances) and is properly phased and baseline-corrected.

• Essential 2D NMR Experiments for CASE:

  • ^1H-^1H COSY: Identifies ^3J_HH coupling networks (geminal and vicinal protons). Use sufficient resolution to resolve overlapping cross-peaks.
  • HSQC (or HMQC): Identifies all direct ^1H-^13C one-bond correlations. This defines the CH, CH_2, and CH_3 groups in the molecule.
  • HMBC: The most critical experiment for CASE. Optimize for ^nJ_CH of ~8 Hz (typical for ^2J and ^3J). Acquire with a long acquisition time in the indirect dimension to maximize resolution. Explicitly note any very weak or potentially long-range (^4J_CH or greater) correlations during data analysis, as these violate standard CASE assumptions and must be treated carefully [1].

• Data Export: Export all processed spectra (peak lists and, ideally, FID data) in a format compatible with the target CASE software (e.g., JCAMP-DX, Bruker, Varian formats).

Protocol 2: Structure Generation & Ranking using a CASE Expert System

Objective: To convert spectroscopic data into a ranked list of candidate structures.

Materials:

• CASE software suite (e.g., ACD/Structure Elucidator, COCON, etc.).
• Processed peak lists from Protocol 1: Molecular formula, ^1H & ^13C chemical shifts, COSY, HSQC, and HMBC correlations.

Procedure:

• Data Input and Validation:
  • Import the molecular formula and all spectral data into the CASE software.
  • Use the software's tools to verify and edit the correlation tables. Manually check that all cross-peaks from the 2D spectra are correctly assigned as COSY (H-H) or HMBC (H-C). Remove any spurious or noise-derived peaks. This step is crucial for accuracy.

• Setting Parameters and Generating Structures:

  • Accept default parameters for bond connectivity (typically allowing C, H, N, O, etc., as defined by the molecular formula).
  • Initiate the structure generation process. The system will use the HMBC and COSY correlations as distance constraints to assemble all possible planar graphs (constitutions).
  • Troubleshooting: If generation yields >1 million structures, constraints are too loose. Re-check HMBC peak assignments. If generation yields zero structures, constraints are too strict or data contains errors; review for missing long-range HMBC correlations or incorrect chemical shift assignments [1].

• Structure Ranking and Analysis:

  • Once generation is complete, the software will rank candidates using internal chemical shift prediction algorithms (often based on incremental rules or HOSE codes).
  • Analyze the top-ranked structures (e.g., the top 10). Examine the average deviation between predicted and experimental chemical shifts for each candidate. A significantly lower deviation for the top candidate provides strong evidence for its correctness.
  • Key Output: The result is not a single structure but a probability-ranked list. The difference in probability or deviation between ranks 1 and 2 is a quantitative measure of confidence [22].

Protocol 3: Validation and Stereochemical Assignment via DFT-NMR Calculations

Objective: To unambiguously confirm the top CASE-derived planar structure and determine its relative/absolute stereochemistry.

Materials:

• Top candidate planar structure(s) from Protocol 2.
• Computational chemistry software with molecular mechanics, conformational search, and DFT capabilities (e.g., Gaussian, ORCA, Schrödinger Suite).
• High-performance computing (HPC) resources.

Procedure:

• Conformational Search:
  • For the top planar candidate, generate all likely stereoisomers.
  • For each stereoisomer, perform a systematic or stochastic conformational search using molecular mechanics (MMFF or similar) to identify all low-energy conformers within a ~5 kcal/mol window.

• DFT Geometry Optimization and NMR Calculation:

  • Select all unique low-energy conformers (typically >1% population) for each stereoisomer.
  • Optimize their geometries using a DFT method (e.g., B3LYP/6-31G(d)).
  • For each optimized conformer, calculate the ^1H and ^13C NMR chemical shifts using the GIAO (Gauge-Independent Atomic Orbital) method with a higher basis set (e.g., B3LYP/6-311+G(2d,p)) and a solvent model (PCM or SMD) [1] [22].

• Averaging and Statistical Analysis (DP4 Protocol):

  • Boltzmann-weight the calculated chemical shifts of all conformers for each stereoisomer to produce a single, averaged set of predicted shifts.
  • Compare these predicted shifts to the experimental shifts using the DP4 probability method or similar statistical analysis.
  • Interpretation: The stereoisomer with the highest DP4 probability (e.g., >99%) is the correct configuration. This combined CASE/DFT approach is considered the modern gold standard for complete NP structure elucidation [22].

Protocol 4: De-replication via Database Mining and Metabolomics

Objective: To rapidly identify known compounds in a mixture prior to full isolation and CASE analysis, saving resources.

Materials:

• Crude or partially purified extract.
• LC-HRMS system.
• Public/commercial NP databases (e.g., GNPS, AntiBase, Chapman & Hall DNP, CAS Content Collection [21] [24]).

Procedure:

• LC-HRMS/MS Data Acquisition:
  • Analyze the extract via LC-HRMS, collecting data-dependent MS/MS spectra for major peaks.
  • Record retention time, accurate mass, and fragmentation patterns.

• In-Silico Database Query:
  • Search the accurate mass of each component against NP databases. A mass match within 5 ppm suggests a potential known compound.
  • For more confidence, compare the experimental MS/MS spectrum with library spectra using platforms like the Global Natural Products Social Molecular Networking (GNPS) [21].
  • CASE Integration: If a component is flagged as potentially novel (no good database match), it becomes a priority target for isolation and the full CASE workflow described in Protocols 1-3.

Visual Workflows and Logical Pathways

Diagram 1: Integrated CASE Workflow for NP Structure Elucidation (Max Width: 760px)

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 2: Key Research Reagent Solutions for CASE-Driven NP Elucidation

Item / Solution	Function / Purpose	Critical Application Notes
Deuterated NMR Solvents (e.g., CD_3OD, DMSO-d_6, CDCl_3)	Provides the locking signal for NMR spectrometers and minimizes interfering ^1H signals from the solvent.	Select solvent that optimally dissolves the NP. Use highest isotopic purity (e.g., 99.96% D) to minimize residual solvent ^1H peaks.
NMR Tube with Coaxial Insert	Allows for the inclusion of a secondary standard (e.g., TMS in CDCl_3) without contaminating the primary sample.	Essential for precise, reproducible chemical shift referencing, a critical input for CASE and DFT calculations.
LC-MS Grade Solvents (MeOH, CH_3CN, H_2O with 0.1% Formic Acid)	Used for sample preparation for HRMS and LC-MS de-replication.	High purity minimizes background ions, ensuring accurate mass determination and clean MS/MS spectra for database matching [25].
Reference Standard for HRMS Calibration (e.g., Sodium Formate, Agilent Tuning Mix)	Enables external or internal mass calibration of the HRMS instrument.	Mandatory for achieving <3 ppm mass accuracy required for definitive molecular formula assignment.
Software Suite: CASE Program (e.g., ACD/SE), NMR Processing, DFT Package	The digital toolkit for data processing, structure generation, and quantum mechanical validation.	Ensure compatibility of spectral data formats between the NMR spectrometer software and the CASE program.
Structural Databases (Commercial: DNP, AntiBase; Public: GNPS, PubChem)	Used for de-replication and to provide prior knowledge for CASE systems' internal libraries.	Regular updates are necessary to include newly reported NPs and avoid redundant "rediscovery" [21] [24].

This application note details the operational framework of Computer-Assisted Structure Elucidation (CASE) within natural products research. CASE systems are expert systems designed to drastically reduce the time and effort required to determine the structures of newly isolated organic compounds by integrating spectroscopic data with logical-combinatorial algorithms [26]. The elucidation process hinges on three fundamental components: the molecular formula, which defines the search space; spectral data (NMR, MS, IR, UV), which provide structural constraints; and structure generators, the algorithmic engines that assemble candidate molecules [26] [27]. Modern advancements are revolutionizing this field, including the integration of vast, open-access spectral and structural databases [28] [29], the development of open-source CASE platforms [30], and the emergence of generative artificial intelligence models capable of end-to-end structure prediction from spectral data [31]. This document provides detailed protocols for contemporary CASE workflows, visualizes the logical architecture, and catalogues the essential toolkit for researchers engaged in the discovery and characterization of natural products.

The structural elucidation of unknown organic compounds, a cornerstone of natural products chemistry and drug discovery, is a complex, multi-stage puzzle. For over half a century, Computer-Assisted Structure Elucidation (CASE) systems have been developed to emulate and enhance the reasoning of an expert spectroscopist [26] [27]. These systems are particularly vital for natural products research, where molecules are often structurally novel, complex, and isolated in minute quantities. The core challenge CASE addresses is combinatorial explosion: a single molecular formula can correspond to billions of possible structural isomers, a number that grows astronomically for medium-sized molecules [26]. CASE strategies navigate this vast chemical space by systematically applying structural constraints derived from experimental spectra to eliminate incorrect candidates. The resurgence of interest in CASE is driven by the increasing availability of high-resolution 2D NMR and mass spectrometry data, powerful open-source software tools [30], and comprehensive public databases like the Natural Products Atlas [29] and GNPS [28]. Furthermore, the field is on the cusp of a paradigm shift with the introduction of deep learning models that promise to bypass traditional combinatorial generation for a direct, predictive approach [31].

Foundational Components and Quantitative Workflows

Molecular Formula: The Search Space Definition

The molecular formula (MF), typically determined via high-resolution mass spectrometry (HR-MS), is the essential starting point for any de novo CASE analysis. It defines the atomic composition of the unknown compound, setting the absolute boundaries of the chemical space to be explored. The MF is a critical filter because the number of possible structural isomers it represents is finite but immense. An expert system uses the MF, along with standard valency rules, as the primary input for its structure generator [27] [32]. Accurate determination is paramount, as an incorrect formula will preclude finding the correct structure.

Table 1: Impact of Molecular Formula Complexity on Isomer Count

Molecular Formula	Compound Class Example	Approximate Number of Possible Structural Isomers	CASE Strategy Implication
C₆H₆O	Simple phenol derivative	~1 Million [26]	Exhaustive structure generation is feasible.
C₁₀H₂₀O	Monoterpenoid	~10¹⁰ (Billions) [26]	Fragment-based generation is necessary to constrain search.
C₂₀H₃₀O₂	Diterpenoid	~10²⁰ – 10³⁰ [26]	Heavy reliance on 2D NMR-derived fragments and constraints is essential.
C₃₀H₄₈O₃	Triterpenoid	>10³⁰ (Avogadro-scale) [26]	Requires maximal spectral constraints and often stochastic or AI-driven generation.

Application Note: For natural products, the MF often provides initial biosynthetic clues (e.g., presence of nitrogen suggesting an alkaloid). When HR-MS data is ambiguous, ¹³C NMR signal count and integration can help validate the proposed formula [27].

Spectral Data: The Source of Structural Constraints

Spectroscopic data act as the "code" that must be deciphered to reveal the molecular structure. Different spectroscopic techniques provide complementary layers of structural information, which the CASE system integrates to form a set of positive and negative constraints [26].

Table 2: Spectral Data Types and Their Informational Role in CASE

Spectroscopic Technique	Key Data Provided	Role in Constraint Generation	Typical Input for Modern CASE
HR-MS	Accurate molecular mass, molecular formula, fragmentation patterns.	Defines the search space (MF). Fragments can suggest substructures [28].	Molecular formula, key fragment m/z values.
¹H & ¹³C NMR	Chemical shifts (δ), signal multiplicity, integration.	Reveals chemical environments, counts of CHₓ groups. Forms the basis for initial atom labeling.	Peak lists (δ, multiplicity) are essential. Processed spectra are used for database dereplication.
2D NMR (HSQC, HMQC)	¹H-¹³C direct (¹JCH) correlations.	Defines all protonated carbons (CH, CH₂, CH₃ groups). Creates a foundational connectivity map.	Peak pair lists (δH, δC) are mandatory inputs for structure assembly.
2D NMR (COSY, TOCSY)	¹H-¹H through-bond (²-³JHH) correlations.	Identifies spin systems and contiguous proton networks.	Correlation lists define proximity between hydrogen atoms.
2D NMR (HMBC)	¹H-¹³C long-range (²-³JCH) correlations.	Connects molecular fragments across quaternary centers and heteroatoms. Most critical for assembling the skeletal structure.	Correlation lists are the primary data for connecting substructures.
IR Spectroscopy	Characteristic functional group absorptions.	Identifies specific bond types (e.g., OH, C=O, C≡N).	Used as a filter to validate or reject candidate structures containing incompatible functional groups.

Protocol 1: Spectral Data Preparation for a Classical CASE System (e.g., Sherlock/StrucEluc)

• Data Acquisition: Acquire a standard set of NMR spectra for the unknown compound: ¹H, ¹³C, DEPT-135, HSQC, HMBC, and COSY [30]. For MS, obtain HR-MS data (ESI or EI).
• Peak Picking and Processing:
  • Process ¹D spectra to generate a list of chemical shifts with accurate multiplicities and integrations.
  • For ²D spectra (HSQC, HMBC, COSY), meticulously pick cross-peaks. In HMBC spectra, note that correlations can be "nonstandard" length (>³JCH), which modern systems can handle using "fuzzy structure generation" [26].
  • Assign solvent signals and exclude them from the peak lists.
• Create a Molecular Connectivity Diagram (MCD): Input the peak lists into the CASE software. The system will generate an MCD, which is an atom-by-atom representation where:
  • Each heavy atom (C, O, N, etc.) and hydrogen from the MF is represented.
  • Atoms are labeled with their chemical shift ranges from ¹D NMR.
  • HSQC correlations define CHₓ groupings.
  • COSY and HMBC correlations are represented as "possible connectivities" between atoms [26] [30].
• Review and Edit MCD: Manually review the automatically generated MCD. Correct any mis-assigned multiplicities (e.g., from HSQC) based on ¹H integration and DEPT data. This step is critical for reducing computational time and false positives [30].

Structure Generators: The Combinatorial Engine

The structure generator is the core algorithmic component of a CASE system. Its function is to assemble all possible constitutional isomers that satisfy three conditions: 1) the input molecular formula, 2) any user-defined or automatically detected structural fragments, and 3) the connectivity constraints derived from 2D NMR correlations [32]. Generators employ advanced graph theory and combinatorial algorithms to navigate the immense isomer space efficiently.

Table 3: Taxonomy and Characteristics of Chemical Structure Generators

Generator Type	Core Principle	Advantages	Limitations	Example Systems
Structure Assembly	Builds molecules bond-by-bond or fragment-by-fragment, starting from atoms or known substructures.	Intuitive; allows integration of spectral fragments early in the process.	Can suffer from combinatorial explosion for large, unconstrained molecules.	ASSEMBLE, GENOA, generators in CHEMICS [32].
Structure Reduction (Orderly Generation)	Generates all possible adjacency matrices for the MF and filters out invalid graphs. Based on mathematical group theory.	Highly efficient and exhaustive; minimal memory overhead for canonical checking.	Less intuitive; harder to integrate intermediate spectral constraints during the generation process.	MOLGEN, MASS [32].
Stochastic / Heuristic	Uses probabilistic methods (e.g., simulated annealing, genetic algorithms) to search the isomer space.	Can find solutions for very large molecules where exhaustive generation is impossible.	Not exhaustive; may miss the correct structure; requires careful tuning of parameters.	SENECA [32].
AI-Based Generative	Uses deep learning models (e.g., Transformers) to directly predict the most probable structure from spectral data as an input-output sequence.	Extremely fast (seconds); learns complex spectral-structure relationships directly from data.	Requires large, high-quality training datasets; "black-box" nature can reduce interpretability.	CLAMS, other Transformer models [31].

Protocol 2: Structure Generation and Ranking Workflow

• Fragment Detection: The system queries its internal libraries or external databases (e.g., of ¹³C NMR subspectra) to propose structural fragments consistent with the spectral data [27]. The user selects which fragments to include as mandatory.
• Generation Execution: Initiate the structure generator with the following inputs: the MF, the edited MCD, and the selected mandatory fragments. For an exhaustive generator, this will produce all valid structures. For complex cases, settings may be adjusted to limit the number of nonstandard HMBC correlations or to use a stochastic approach [26] [30].
• Spectral Prediction and Ranking: The software predicts NMR spectra (typically ¹³C and ¹H chemical shifts) for each candidate structure using internal algorithms (e.g., HOSE code-based or neural networks). Each candidate is ranked based on the deviation (e.g., mean average error) between its predicted spectrum and the experimental data [30].
• Result Analysis: Examine the top-ranked candidates. A correct solution is strongly indicated if one structure is significantly better (lower deviation) than all others. The software should provide a full spectral assignment for the top candidate[s].

Advanced Protocols and Integrative Tools

Protocol: AI-Driven Structural Elucidation with Transformer Models

Emerging generative AI models like CLAMS represent a paradigm shift, replacing multi-step combinatorial workflows with a single end-to-end prediction [31].

• Data Preparation: For a model like CLAMS, spectroscopic data (IR, UV, 1H NMR) must be formatted into a standardized 1D array, which is then reshaped into a 2D "image" format suitable for a Vision Transformer encoder [31].
• Model Input: The formatted spectral image is fed into the trained encoder-decoder Transformer model.
• Sequence Generation: The decoder generates a SMILES string autoregressively, token-by-token, based on the encoded spectral input.
• Output: The most probable SMILES string(s) are output as the predicted structure(s). The model achieves a top-15 accuracy of 83% for molecules up to 29 atoms within seconds on a CPU [31].

Protocol: Database-Integrated Dereplication and Variant Discovery

Before de novo elucidation, scientists use databases to check if a compound is known, a process called dereplication. Advanced algorithms like VInSMoC now enable the discovery of structural variants [28].

• Database Search: Submit an experimental high-resolution MS/MS spectrum to a platform like GNPS.
• Variant-Tolerant Matching: Use an algorithm like VInSMoC, which performs a "variable mode" search against databases (e.g., PubChem, COCONUT, Natural Products Atlas [29]). It estimates statistical significance to find not only exact matches but also structurally related variants (e.g., methylated, hydroxylated analogs).
• Pathway Analysis: For microbial natural products, link identified variants to biosynthetic gene cluster information from integrated resources like MIBiG to propose biosynthetic pathways [28].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 4: Key Software, Databases, and Tools for Modern CASE

Tool/Resource Name	Type	Primary Function in CASE	Access / Reference
ACD/Structure Elucidator	Commercial CASE Suite	Integrated platform for processing spectra, generating MCDs, exhaustive structure generation, and ranking. A long-standing industry standard [26].	Commercial
Sherlock	Open-Source CASE System	Provides a graphical workflow from peak picking to structure generation and ranking, emphasizing user control and traceability [30].	Open Source [30]
GNPS (Global Natural Products Social Molecular Networking)	Public MS/MS Data Platform	Repository and ecosystem for experimental mass spectra. Enables dereplication, molecular networking, and variant searching via tools like VInSMoC [28].	Open Access
Natural Products Atlas	Curated Structural Database	Open-access database of microbially-derived natural product structures. Serves as a critical reference for dereplication and training AI models [29].	Open Access [29]
CLAMS & Similar AI Models	Generative AI Software	Proof-of-concept transformer models that directly predict molecular structures from spectroscopic data, offering extreme speed [31].	Research Code [31]
MOLGEN	Chemical Graph Generator	A highly efficient, exhaustive structure generator based on orderly generation, used as a backend in some CASE systems [32].	Commercial / Academic
PubChem / COCONUT	Large-Scale Compound Databases	Sources of millions of chemical structures used for large-scale database searches in mass spectrometry-based annotation [28].	Open Access

Step-by-Step CASE Workflows and Real-World Applications in Drug Discovery

Computer-Assisted Structure Elucidation (CASE) represents a transformative paradigm in natural products research, systematically addressing the critical bottlenecks of dereplication and the determination of complex stereochemistry [33]. By integrating advanced spectroscopic data with computational algorithms, CASE systems accelerate the transition from raw analytical data to confident structural proposals. This application note details a standardized, instrument-agnostic workflow encompassing data preparation, automated peak picking, candidate structure generation, and probabilistic ranking, providing researchers with a reproducible framework for elucidating novel natural products [34] [35].

A successful CASE workflow begins with the selection of appropriate software and the acquisition of a foundational NMR dataset. The following tables summarize key platforms and the essential spectroscopic data required for de novo elucidation.

Table 1: Comparison of Representative CASE Software Platforms

Software Platform	Core Functionality	Key Workflow Feature	Primary Application
ACD/Structure Elucidator Suite [34]	De novo structure generation, 3D configuration from NOESY/ROESY, database dereplication.	Automated Molecular Connectivity Diagram (MCD) generation and editing, real-time structure ranking.	Elucidation of complex, unknown natural products.
Mnova Structure Elucidation [35]	Integrated NMR processing and CASE in a single environment.	Six-step guided workflow from data input to structure ranking.	Streamlined elucidation for both experts and non-experts.
MS-DIAL / MZmine [36]	LC-MS/MS data processing, peak picking, and alignment for metabolomics.	Feature detection, decomposition, and identification.	Untargeted metabolomics profiling and dereplication.

Table 2: Minimum Recommended NMR Data for CASE Analysis [34]

Experiment Type	Information Provided	Role in CASE Workflow
¹H NMR	Chemical shift, integration, coupling constants.	Defines proton count and local electronic environment.
¹³C NMR	Chemical shift, multiplicity (DEPT).	Defines carbon count and hybridization (sp³, sp², sp).
COSY	Through-bond ¹H-¹H couplings (²J, ³J).	Establishes proton connectivity networks.
HSQC	One-bond ¹H-¹³C correlations.	Directly links protons to their bonded carbons.
HMBC	Long-range ¹H-¹³C correlations (²J, ³J).	Establishes linkages between structural fragments, crucial for assembling the carbon skeleton.

Detailed Experimental Protocols

Protocol: Data Input and Spectral Pre-processing

Objective: To prepare and import standardized, high-quality spectral data into the CASE software.

• Molecular Formula Determination: Input the molecular formula derived from high-resolution mass spectrometry (HR-MS). This defines the search space for structure generation [34].
• Data Import and Alignment: Import raw or processed 1D and 2D NMR data (vendor-agnostic formats supported). Use software tools to align 1D traces with 2D spectra and ensure consistent referencing across all datasets [34].
• Initial Processing: Apply standard processing (Fourier transformation, window functions, phasing, baseline correction) to optimize spectral clarity [34] [35].

Protocol: Robust Peak Picking for 2D NMR Correlations

Objective: To accurately identify cross-peaks in 2D spectra (HSQC, HMBC, COSY) which form the basis of structural constraints [36].

• Software Selection: Utilize the integrated peak picking tools within the CASE suite (e.g., Mnova [35]) or dedicated pre-processing software.
• Parameter Optimization: Adjust sensitivity thresholds to maximize true positive correlations while minimizing noise. For HMBC, pay special attention to setting appropriate intensity thresholds for weaker long-range correlations.
• Validation and Editing: Manually review automated peak lists. Validate correlations against expected chemical shift ranges and remove obvious artifacts. Studies indicate that manual validation can significantly improve the true positive rate of features [36].
• Contour Level Consistency: Ensure consistent contour levels are used across all spectra for reliable comparative analysis.

Protocol: Structure Generation and Ranking

Objective: To algorithmically generate all plausible structures consistent with the data and identify the most probable candidate [34].

• Molecular Connectivity Diagram (MCD) Creation: The software automatically generates an MCD—a 2D map of atoms (colored by hybridization) and the correlations between them [34].
• MCD Editing and Constraint Application: Manually review and edit the MCD. Add known fragments (obligatory bonds) from database searches or prior knowledge. Apply constraints such as forbidden bonds, ring sizes, or functional groups based on chemical intuition or other spectroscopic data (e.g., IR) [34].
• Structure Generation: Initiate the structure generator. The software uses the MCD and molecular formula to assemble all possible constitutional isomers that satisfy the correlation data and applied constraints [34].
• Chemical Shift Prediction & Ranking: The software predicts ¹³C (and ¹H) NMR chemical shifts for each candidate using internal databases (HOSE codes) or neural networks. Candidates are ranked based on the average deviation (dᵥ) between predicted and experimental shifts [34].
• Probabilistic Validation: For the top-ranked candidates, apply statistical measures like the DP4 probability to assess confidence. The DP4 metric calculates the probability that a given candidate is correct based on the combined uncertainty of all chemical shift predictions [34].

Research Reagent Solutions & Essential Materials

Table 3: Essential Software and Materials for CASE Workflow

Item	Function/Description	Application Note
CASE Software Suite (e.g., ACD/SE, Mnova) [34] [35]	Integrated platform for data processing, structure generation, and ranking.	The core tool for executing the workflow; selection depends on lab preference and required features.
High-Resolution Mass Spectrometer	Provides accurate molecular formula.	Essential first step to constrain the structure generation problem.
NMR Spectrometer (≥ 400 MHz)	Generates the 1D and 2D correlation data.	A minimum set of ¹H, ¹³C, HSQC, HMBC, and COSY is recommended [34].
Reference Compound (e.g., TMS)	Provides chemical shift reference (0 ppm).	Critical for accurate chemical shift alignment across all spectra.
Deuterated Solvent (e.g., CDCl₃, DMSO-d₆)	NMR solvent.	Choice affects chemical shifts and must be considered in prediction algorithms.
Fragment/Structure Database (e.g., PubChem, internal libraries) [34]	Aids in dereplication and provides fragments for MCD.	Used to avoid "rediscovering" known compounds and to seed the MCD with known substructures.

Workflow Visualization

Diagram 1: Standardized CASE Workflow for Natural Products

Diagram 2: Peak Picking and Validation Protocol

Within the discipline of natural products research, the unequivocal determination of molecular structure is paramount, as it defines biological activity and guides synthetic and medicinal chemistry efforts. Despite advancements in spectroscopic techniques, structural misassignment remains a persistent issue in the literature, underscoring the need for rigorous analytical methodologies [37]. This article details the construction and application of the Molecular Connectivity Diagram (MCD), a critical intermediate representation in Computer-Assisted Structure Elucidation (CASE) systems. The MCD operationalizes raw NMR correlation data into a set of explicit structural constraints, forming the foundational dataset from which all plausible chemical skeletons are generated [38].

The broader thesis posits that integrating CASE methodologies into the natural product discovery workflow is essential for enhancing accuracy, efficiency, and reproducibility. The evolution of NMR from a tool for analyzing pure compounds to one capable of direct mixture analysis (e.g., via DOSY, STOCSY) further amplifies the need for robust computational frameworks to manage complexity [39]. The MCD sits at the heart of this integration, translating ambiguous spectral peaks into a logical map of atom-to-atom connections.

Foundational NMR Data for MCD Construction

The MCD is built from a core set of one- and two-dimensional NMR experiments. Accurate interpretation of these spectra is a prerequisite for generating a reliable MCD.

Essential 1D and 2D NMR Experiments

A standard suite of experiments provides the necessary data for basic structure elucidation [38].

Table 1: Core NMR Experiments for MCD Generation

Experiment	Nuclei Correlated	Key Structural Information Provided	Typical Constraint in MCD
¹H NMR	--	Chemical shift (δ), integration (H count), multiplicity (neighbors) [40] [41].	Defines hydrogen atoms and their local environments.
¹³C NMR	--	Chemical shift (δ), identifies number of unique carbons.	Defines carbon atom types (C, CH, CH₂, CH₃).
HSQC (or HMQC)	¹H→¹³C (one-bond)	Directly pairs each proton to its bonded carbon atom.	Crucial: Defines the heavy atom (C, N, O) skeleton with attached hydrogens.
COSY	¹H→¹H (2-3 bonds)	Identifies protons that are coupled to each other through 2-3 bonds (geminal or vicinal).	Establishes connectivities between protonated atoms (e.g., CH-CH).
HMBC	¹H→¹³C (2-4 bonds)	Correlates protons to carbons separated by 2-4 bonds (including long-range).	Establishes key linkages between structural fragments, often across heteroatoms or quaternary carbons.

Interpreting Correlations as Structural Constraints

Each peak in a 2D spectrum represents a correlation between two nuclei. The MCD creation process involves translating these correlations into connectivity rules [38]:

• A COSY correlation between proton H(A) and proton H(B) implies that their attached carbons, C(A) and C(B), are separated by no more than three bonds (typically 2 or 3 bonds).
• An HMBC correlation between proton H(A) and carbon C(B) implies the two atoms are separated by 2-4 bonds. This is critical for connecting fragments through quaternary carbons or heteroatoms.

Protocol: From Spectra to Molecular Connectivity Diagram

The following protocol details the steps for transforming processed NMR spectra into a refined MCD suitable for structure generation in a CASE system [38] [42].

Step 1: Data Input and Preprocessing

• Input Molecular Formula: Enter the exact molecular formula, typically determined by High-Resolution Mass Spectrometry (HRMS). This defines the total atom count and degrees of unsaturation.
• Load and Process Spectra: Import the raw or processed 1D and 2D NMR data (¹H, ¹³C, HSQC, HMBC, COSY). The software performs peak picking to create a table of chemical shifts, multiplicities, and correlations [38] [43].
• Verify and Edit Peak Lists: Manually inspect and correct the automated peak picking. Remove noise and solvent artifacts. This step is critical, as errors here propagate through the entire elucidation process [42].

Step 2: Atom Labeling and Fragment Definition

• Generate HSQC-Based Fragments: Using the HSQC data, the software defines atom labels for each unique ¹H/¹³C pair. Each carbon is classified as CH, CH₂, CH₃, or C (quaternary). Heteroatoms (N, O, etc.) are introduced based on the molecular formula and chemical shift rules [38].
• Annotate with Chemical Shifts: Each atom in the developing MCD is tagged with its experimental ¹³C and ¹H chemical shifts.

Step 3: Correlation Mapping and MCD Assembly

• Map COSY Correlations: Draw blue arrows or lines (conventional color) between hydrogen atoms on the MCD based on COSY correlations. These represent bonds or short pathways between proton-bearing carbons.
• Map HMBC Correlations: Draw green arrows or lines (conventional color) from a hydrogen atom to a carbon atom based on HMBC correlations. These represent 2-4 bond connectivities [38].
• Define Atom Properties: The software uses empirical rules to annotate each atom with likely properties: hybridization (sp³, sp², sp), possible attachment to heteroatoms, and membership in functional groups (e.g., carbonyl, aromatic ring).

Diagram 1: MCD Construction Workflow (85 characters)

This interactive step is where researcher expertise is critical [38] [42].

• Review Connectivity Arrows: Validate all auto-generated COSY and HMBC connections. Remove correlations attributed to common artifacts (e.g., strong coupling effects, residual solvent) or non-standard correlations (NSCs) if not using fuzzy logic.
• Add Structural Constraints: Introduce known chemical information:
  • Sample origin (e.g., plausible biogenetic precursors).
  • Functional group evidence from IR or UV spectroscopy.
  • Stereochemical insights from NOESY/ROESY or coupling constants.
• Set Atom Properties Manually: Override automated predictions when evidence is strong (e.g., lock a carbon atom as a carbonyl based on its ¹³C shift > 200 ppm).

Protocol: Structure Generation, Ranking, and Validation from an MCD

Once a validated MCD exists, the CASE system uses it to generate and rank all possible structural isomers.

Step 1: Structure Generation

• Choose Generation Mode:
  • Strict Mode: Assumes all HMBC correlations are standard 2-3 bond lengths. Faster but may fail if NSCs are present [38].
  • Fuzzy Structure Generation (FSG): Allows for user-defined or program-identified NSCs of unknown length. Generates more structures but is more robust for complex molecules [38].
• Execute Generation: The algorithm assembles all constitutional isomers that satisfy every connectivity constraint in the MCD and the molecular formula. The number of generated structures (k) can range from one to hundreds of thousands, depending on complexity and data constraints [38].

Step 2: Structural Filtering and Ranking

• Initial Filtration: Generated structures are filtered using structural criteria (e.g., forbidden fragments, unstable rings) and spectral criteria (e.g., gross deviations from predicted chemical shifts). This reduces the list to a manageable number (e.g., 100) [38].
• Chemical Shift Prediction and Ranking: ¹³C NMR spectra are predicted for the filtered structures using fast incremental or neural network algorithms (speed: 10-30k shifts/sec). Structures are ranked by the average deviation (d_N) between experimental and predicted shifts [38].
• Advanced Ranking with HOSE Codes: The top candidates (10-50) are re-evaluated using the more accurate Hierarchical Organization of Spherical Environments (HOSE) code method, generating a new average deviation (dA). The structure with the lowest dA is typically the correct one [38].

Step 3: Validation and Reporting

• Top Candidate Inspection: The researcher examines the top-ranked structure(s) for chemical sense and consistency with all available data.
• DFT-Level Verification (Gold Standard): For final validation, especially for complex or novel scaffolds, perform Density Functional Theory (DFT) calculations to predict NMR parameters with high accuracy. Statistical measures (e.g., DP4+ probability, MAE) quantify the match between DFT-predicted and experimental shifts [37] [44].
• Reporting: The software generates a report containing the elucidated structure, full atomic assignments, a list of correlated spectra, and the statistical metrics of fit [42].

Diagram 2: MCD to Validated Structure (80 characters)

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Tools for CASE and MCD-Based Elucidation

Tool Category	Specific Item / Software	Function in MCD Workflow	Key Considerations
NMR Experiments	HSQC, HMBC, COSY	Provide the essential ¹H-¹³C and ¹H-¹H connectivity data.	Quality of 2D data is the primary limiting factor for MCD accuracy.
CASE Software	ACD/Structure Elucidator [38], Bruker CMC-se [42], MNova Structure Elucidation	Platform for creating, editing the MCD, and generating/ranking structures.	Differ in user interface, algorithm speed, and handling of non-standard correlations.
NMR Prediction Engines	HOSE Code Algorithms [38], Neural Networks, DFT (GIAO)	Provide the predicted chemical shifts used to rank candidate structures.	Speed vs. Accuracy trade-off: HOSE/Neural nets (fast) for ranking, DFT (slow, accurate) for final validation.
Machine Learning & AI	ShiftML, IMPRESSION [44]	ML models trained on quantum-chemical data predict NMR shifts at near-DFT accuracy with drastically reduced compute time.	Represents the cutting edge for accelerating the prediction and validation cycle [44].
Data Format & Exchange	NMReData Initiative [42]	Standardized format for reporting and sharing NMR assignments and correlations.	Promotes reproducibility and allows for the creation of shared, high-quality spectral databases.

Advanced Topics and Future Directions

• Machine Learning Integration: The future of CASE involves tighter integration of ML at multiple stages: for initial spectral analysis and peak picking, for chemical shift prediction (as in ShiftML) [44], and for directly scoring candidate structures against raw spectral data.
• Handling Complex Mixtures: Advanced NMR methods like DOSY and STOCSY, used for direct extract analysis, generate data on multiple components simultaneously [39]. Next-generation CASE systems will need to parse this data into separate, component-specific MCDs.
• Stereochemistry Determination: While traditional CASE outputs planar structures, new programs are emerging that utilize J-coupling constants and NOE/ROE data to generate and rank stereoisomers, providing a more complete 3D structural solution [37].

The Molecular Connectivity Diagram is the indispensable conceptual and computational bridge between experimental NMR correlations and the chemical structure of an unknown natural product. By following standardized protocols for MCD construction and refinement, researchers can leverage CASE systems to exhaustively and objectively generate all plausible structures consistent with the data, thereby minimizing the risk of misassignment. As the field advances with machine learning and automated workflows, the MCD will remain the fundamental representation ensuring that computational power is effectively guided by, and accountable to, experimental spectroscopic evidence.

Structure Generation and Candidate Ranking Using Chemical Shift Prediction Algorithms

The structure elucidation of complex natural products remains a formidable challenge in chemical research. Nuclear Magnetic Resonance (NMR) spectroscopy is the definitive analytical tool for this task, but its interpretation is often non-trivial and prone to error [37]. Computer-Assisted Structure Elucidation (CASE) systems have emerged as indispensable partners to the spectroscopist, transforming raw spectral data into probable chemical structures. A critical bottleneck in this pipeline has been the reliance on sparse experimental NMR reference libraries. For known natural products, fewer than 7% have experimentally assigned NMR spectra available in databases [45].

This gap is bridged by in silico chemical shift prediction. Within a broader thesis on advancing CASE for natural products research, the integration of high-accuracy prediction algorithms represents a paradigm shift. These algorithms enable the creation of vast, virtual spectral libraries and provide a robust, quantitative metric for ranking computer-generated structural candidates against experimental data. Modern approaches, leveraging machine learning (ML) and quantum mechanics, have moved prediction accuracy from a heuristic aid to a reliable computational assay [44]. This document details the application notes and experimental protocols for employing these algorithms in a contemporary CASE workflow, focusing on the critical steps of structure generation and candidate ranking to achieve confident and efficient structural determinations.

Core Algorithms and Quantitative Performance

Chemical shift prediction methodologies have evolved from empirical rules to sophisticated data-driven models. The performance of these algorithms, measured by the mean absolute error (MAE) between predicted and experimental shifts, directly impacts their utility in discriminating between similar structural candidates.

Table 1: Performance Comparison of Contemporary Chemical Shift Prediction Methods

Method (Algorithm)	Type	Key Features	Reported MAE (¹H)	Reported MAE (¹³C)	Primary Application Context
PROSPRE [45]	ML (Graph Neural Network)	"Solvent-aware"; trained on curated experimental data; transfer learning.	< 0.10 ppm	Not specified (¹³C upcoming)	Small molecules in solution (H₂O, CDCl₃, DMSO, MeOD).
CSTShift [46]	Hybrid 3D-GNN/DFT	Incorporates DFT-calculated shielding tensor descriptors; uses 3D molecular geometry.	0.185 ppm	0.944 ppm	Small organic molecules; excels with 3D conformational data.
UCBShift [47]	Hybrid (Transfer Learning + Random Forest)	Combines sequence/structure alignment with feature-based ML; robust to outliers.	0.31 ppm (amide H)	0.81-1.81 ppm (Cα, Cβ, C', N)	Proteins and biological macromolecules.
HOSE Code-Based [45] [48]	Empirical (Similarity)	Hierarchical Ordered Spherical description of Environment; lookup of similar substructures.	0.2 – 0.3 ppm	~1.7 – 3.8 ppm	Small molecules, rapid prediction for common fragments.
DFT/GIAO (B3LYP) [46]	Quantum Mechanical	Ab initio density functional theory calculations; considered a high-accuracy benchmark.	~0.2 – 0.4 ppm	~2.0 – 4.0 ppm	Small to medium molecules; high computational cost.
ShiftML/IMPRESSION [44]	ML (Gaussian Process / Neural Network)	Trained on DFT-calculated shifts for solids/liquids; bypasses experimental database errors.	~0.49 ppm	~4.3 ppm	Molecular crystals (ShiftML) & solution-state (IMPRESSION).

The evolution is clear: modern ML and hybrid models like PROSPRE and CSTShift now match or surpass the accuracy of traditional DFT at a fraction of the computational time, making them feasible for high-throughput ranking of thousands of candidate structures [45] [46]. The choice of algorithm depends on the molecular system (small molecule vs. protein), the availability of 3D conformational data, and the required balance between speed and ultra-high accuracy.

Experimental Protocols and Application Notes

Protocol A: Integrated CASE Workflow with Chemical Shift Ranking

This protocol outlines the end-to-end process for elucidating an unknown natural product using a CASE system augmented by predictive ranking.

Objective: To generate all plausible structural isomers consistent with 2D NMR data and identify the single best candidate through quantitative chemical shift comparison.

Input Requirements:

• Experimental ¹H and ¹³C NMR spectra (1D).
• Key 2D NMR correlations (HSQC, HMBC, COSY).
• Molecular formula (from high-resolution mass spectrometry).

Procedure:

• Data Pre-processing & Atom Mapping:
  • Assign all ¹H and ¹³C chemical shifts from the 1D spectra. Label them (e.g., H-1, C-1).
  • Input the molecular formula and the lists of proton and carbon shifts into the CASE software (e.g., ACD/Structure Elucidator, SENECA, or others).
  • Define molecular connectivity constraints by inputting atom-atom correlations from 2D experiments. Standard settings assume HMBC correlations span 2-3 bonds, but this may be adjusted for specific classes like polyketides [37].

• Structure Generation:

  • Execute the structure generation module. The software will use the molecular formula and connectivity constraints to exhaustively construct all constitutional isomers that do not violate the input data.
  • Output: A list (often numbering in the hundreds or thousands) of candidate structures in a machine-readable format (e.g., SDF, MOL).

• Chemical Shift Prediction & Ranking:

  • Prediction: Submit the SDF file of candidate structures to a chemical shift prediction server (e.g., PROSPRE [45]) or use an integrated prediction module. Specify the appropriate NMR solvent to match experimental conditions.
  • Calculation of Deviation: For each candidate, calculate the difference (Δδ) between its predicted shifts and the experimental shifts. The most common metric is the Mean Absolute Error (MAE) or a corrected sum of squared deviations.
  • Statistical Ranking: Rank all candidates in ascending order of their Δδ (lowest error is best). Advanced methods like DP4 probability analysis can be applied to assess the statistical confidence that the top-ranked structure is correct [44].

• Validation & Reporting:

  • Visually inspect the top 3-5 ranked structures. Verify that their predicted shift patterns and long-range correlations logically match the experimental 2D data.
  • For the top candidate, consider performing a higher-level DFT-NMR calculation on its lowest-energy conformation(s) for final confirmation, especially if stereochemistry is in question.
  • Report the elucidated structure alongside the ranking statistics (e.g., MAE, DP4 probability) as evidence supporting the assignment.

Diagram: Computer-Assisted Structure Elucidation (CASE) workflow integrating chemical shift prediction for candidate ranking.

Protocol B: Building a Hybrid Prediction Model for Specialized Compound Classes

This protocol is for research groups aiming to develop or fine-tune a chemical shift predictor for a specific family of natural products (e.g., flavonoids, terpenoids) where general models may lack sufficient training data.

Objective: To create a specialized prediction model with optimized accuracy for a target chemical space by leveraging both public data and private experimental measurements.

Input Requirements:

• A curated set of structures (SMILES or SDF) and their associated, reliably assigned experimental chemical shifts for the target compound class.
• Access to computational resources (CPU/GPU cluster).
• Programming environment (Python with libraries like PyTorch, RDKit).

Procedure:

• Dataset Curation & Augmentation:
  • Compile experimental data from public databases (NMRShiftDB2, NP-MRD [45]) and in-house measurements.
  • Critical Step: Apply rigorous data curation: correct referencing errors, specify solvent, and remove misassigned shifts [45]. For 3D-aware models like CSTShift, generate representative conformers for each molecule [46].
  • Split data into training (~70-80%), validation (~10-15%), and hold-out test sets (~10-15%).

• Model Selection & Architecture:

  • For small datasets (<5,000 samples): Use a graph neural network (GNN) architecture designed for "small data" performance, which may outperform simpler models like HOSE codes and larger GNNs [48].
  • For larger datasets: Implement a state-of-the-art 3D-GNN (like the backbone of CSTShift [46]) or a transformer-based architecture.
  • Hybrid Approach: Consider a transfer learning strategy. Start with a model pre-trained on a large, diverse dataset (e.g., QM9NMR with DFT shifts [49]), then fine-tune it on your specialized, experimentally derived dataset [45].

• Training & Optimization:

  • Implement a training loop minimizing the MAE loss function.
  • Use the validation set for hyperparameter tuning (learning rate, network depth, dropout).
  • Employ early stopping to prevent overfitting.

• Evaluation & Deployment:

  • Evaluate the final model on the held-out test set. Report MAE and RMSE for ¹H and ¹³C.
  • Deploy the trained model as a web service (e.g., using a Flask API) or integrate it directly into your local CASE pipeline for on-demand prediction.

Diagram: Architecture of a hybrid chemical shift prediction model combining multiple algorithmic approaches.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Digital Tools & Resources for CASE with Chemical Shift Prediction

Tool/Resource	Type	Primary Function in Workflow	Key Utility
ACD/Structure Elucidator	Commercial Software Suite	Automated structure generation from NMR data.	Integrates prediction and ranking; industry-standard platform [37].
CENSA	CASE Program	Structure elucidation using 1D/2D NMR data.	Handles complex natural products with few protons [37].
PROSPRE [45]	Web-based Predictor	Predicts ¹H shifts for submitted structures (SMILES/SDF).	High accuracy (<0.10 ppm MAE); accounts for solvent effects.
NMRShiftDB2 / NP-MRD [45]	Open-Access Database	Repository of experimental NMR data for organic molecules & natural products.	Source for training data and experimental comparisons; <7% coverage of known NPs [45].
RDKit	Open-Source Cheminformatics	Python library for molecule manipulation and descriptor calculation.	Essential for preprocessing structures, generating conformers, and building custom ML pipelines [46] [48].
Gaussian / ORCA	Quantum Chemistry Software	Performs DFT calculations for geometry optimization and NMR shielding.	Provides benchmark-quality shifts and shielding tensors for hybrid models like CSTShift [46].
PyTorch / TensorFlow	ML Frameworks	Libraries for building and training neural network models.	Used to develop and deploy custom graph neural network predictors [46] [48].

Implementation Considerations and Future Perspectives

Integrating predictive algorithms into a CASE workflow requires careful consideration. Data quality is paramount: the "garbage in, garbage out" principle applies fully. Inconsistent referencing or incorrect solvent specification in training data can cripple a model's accuracy [45]. Computational cost must be balanced against project needs. While HOSE codes are instantaneous and DFT is computationally intensive, modern ML models like PROSPRE offer a near-optimal balance for high-throughput ranking [45].

The future lies in increasingly integrated and intelligent systems. We will see tighter coupling between ab initio structure generators and predictors that provide real-time feedback to constrain the search space. The rise of generative AI models could invert the process, proposing novel structures that perfectly match an experimental NMR spectrum. Furthermore, the expansion of FAIR (Findable, Accessible, Interoperable, Reusable) databases like NP-MRD will provide the high-quality, solvent-specific data needed to train the next generation of ultra-accurate, chemistry-aware predictors [45]. For the natural products researcher, these advances promise to transform structure elucidation from a time-consuming puzzle into a more streamlined, confident, and discovery-focused process.

The structural elucidation of complex natural products remains a formidable challenge in chemical and pharmaceutical research. Despite advances in spectroscopic techniques, a disturbing number of incorrect structures continue to be reported in the literature, often due to the inherent complexity of these molecules and the subjective interpretation of spectral data [37]. Computer-Assisted Structure Elucidation (CASE) has emerged as an indispensable framework to minimize this risk by systematically generating all chemically plausible structures consistent with experimental data and ranking them probabilistically [37]. Within a broader thesis on CASE, this article details practical protocols and application notes, demonstrating how integrated computational workflows are transforming the accuracy, speed, and scalability of natural product structure determination and revision.

The evolution of CASE now extends beyond traditional rule-based systems. The field is being reshaped by the integration of high-throughput spectral data with advanced algorithms for molecular networking, database mining, and machine learning. These approaches address core challenges such as the presence of long-range correlations in NMR, the identification of novel molecular variants, and the dereplication of known compounds [50] [28]. The following sections provide researchers with actionable methodologies and a critical analysis of tools for applying CASE to real-world problems in natural product research and drug development.

Core Principles and Quantitative Benchmarks of Modern CASE

Modern CASE systems are built on core computational principles that transform raw spectral data into reliable structural hypotheses. A key foundation is the generation of all possible structural isomers within user-defined constraints (e.g., molecular formula, substructure presence/absence) that satisfy the observed spectroscopic correlations [37]. The process typically utilizes 2D NMR data—such as COSY and HMBC correlations—with an initial assumption that correlations represent atoms connected by no more than three bonds, though algorithms must account for longer-range correlations to avoid errors [37].

The performance of different CASE strategies can be quantitatively evaluated. Benchmarking studies reveal the effectiveness of various algorithmic approaches, as summarized in the table below.

Table 1: Performance Benchmarks of Contemporary CASE and Spectral Analysis Algorithms

Algorithm/Approach	Core Function	Reported Performance Metric	Key Advantage
Traditional HSQC Lookup [50]	Spectral similarity search via modified Hungarian distance metric.	Recovers ~70-80% of structural similarity; efficiency plateaus with library size.	Establishes baseline for NMR-based dereplication.
NMR Molecular Networking [50]	Transitive annotation propagation across HSQC spectral networks.	Enables dereplication and accelerates unknown identification in case studies.	Leverages spectral relationships for scalable annotation.
Algorithmic Molecular Networking [50]	Integrates graph topology metrics to correct spectral rankings.	Reduces false positives and improves ranking efficiency.	Uses network structure to enhance reliability.
VInSMoC (MS/MS Search) [28]	Database search for molecular variants with statistical significance.	Identified 43,000 knowns & 85,000 unreported variants from 483M spectra.	Enables discovery of novel variants beyond exact matching.

The transition from standalone structure generators to integrated, data-rich workflows represents the current paradigm. As shown in Table 1, newer methods like NMR molecular networking and variant-tolerant MS/MS search focus on leveraging large-scale spectral relationships and probabilistic scoring, moving beyond simple library lookup [50] [28]. This shift addresses the critical need to identify both known compounds and novel structural classes efficiently within complex metabolomic mixtures.

Application Note 1: Structure Revision via NMR Molecular Networking and Algorithmic Ranking

Background & Objective: This protocol addresses the challenge of dereplicating known compounds and elucidating novel analogues within a series of related natural product fractions using 2D NMR data. Traditional methods struggle with spectral ambiguity and long-range correlations [37]. The workflow leverages the principles of NMR molecular networking, which applies transitive annotation and graph-based metrics to improve accuracy [50].

Experimental Protocol:

• Data Acquisition & Preprocessing:

  • Acquire standardized ( ^1 \text{H} )-( ^{13} \text{C} ) HSQC spectra for all purified fractions and relevant reference compounds.
  • Process spectra uniformly (phasing, baseline correction). Precisely pick peaks to generate a list of (δH, δC) coordinates for each sample.
  • Format data into a peak table where each row represents a sample and columns contain the list of peak coordinates.

• Spectral Distance Calculation & Network Construction:

  • Calculate pairwise spectral similarity between all samples using a modified Hungarian algorithm to match peaks between two spectra [50]. This metric optimally matches peaks from one spectrum to another to compute a maximal similarity score.
  • Construct a molecular network where each node is a sample (HSQC spectrum). Connect nodes (spectra) with edges if their pairwise similarity score exceeds a defined threshold (e.g., top 10% of all scores or a fixed cosine score > 0.7).
  • Visualize the network using graph visualization software (e.g., Cytoscape) to observe spectral clusters.

• Annotation Propagation & Dereplication:

  • Annotate nodes for which the structure is known (e.g., pure reference standards or previously identified compounds).
  • Propagate annotations within the network. An unknown spectrum connected to a known, annotated spectrum with high confidence is assigned the same or a closely related structural annotation. This step effectively dereplicates known compounds [50].

• Algorithmic Ranking for Novel Analogues:

  • For clusters without definitive annotations, employ algorithmic molecular networking [50].
  • Extract graph topology metrics (e.g., node centrality, edge density) for the cluster. Use these metrics to inform and correct the ranking of potential candidate structures generated by a traditional CASE program.
  • Input the HSQC peak list of the unknown into a CASE engine (e.g., ACD/Structure Elucidator, etc.) to generate a ranked list of candidate structures.
  • Re-rank candidates by integrating their predicted HSQC spectra with the network's topological context, prioritizing structures that best explain the unknown's position and connectivity within the spectral network.

• Validation: Confirm the top-ranked structure through complementary data (e.g., HMBC, COSY) and/or by comparison of predicted vs. observed chemical shifts using quantum mechanical calculations (e.g., DFT).

The Scientist's Toolkit for NMR-Based CASE:

• 2D NMR Spectrometer (e.g., 500-800 MHz): For acquiring high-resolution, sensitive HSQC, HMBC, and COSY data.
• Spectral Processing Software (e.g., MestReNova, TopSpin): For consistent data preprocessing and peak picking.
• CASE Software Suite (e.g., ACD/Structure Elucidator, Bruker CMC-se): Core engine for structure generation from spectral constraints.
• NMR Molecular Networking Scripts (Custom or published): To implement modified Hungarian distance and network construction [50].
• Graph Visualization & Analysis Platform (e.g., Cytoscape): To visualize and analyze spectral networks.
• Quantum Chemistry Software (e.g., Gaussian, NWChem): For DFT-based chemical shift prediction and final validation.

The following diagram illustrates the logical workflow and decision points in this NMR-based structure elucidation protocol.

Application Note 2: LC-MS/MS-Based Variant Identification with VInSMoC

Background & Objective: Mass spectrometry-based metabolomics generates vast datasets where the goal is to identify not only exact matches to known compounds but also structural variants and novel analogues. This protocol utilizes the VInSMoC (Variable Interpretation of Spectrum–Molecule Couples) algorithm to search experimental MS/MS spectra against large molecular databases in a modification-tolerant manner, facilitating the discovery of new natural product variants [28].

Experimental Protocol:

• LC-MS/MS Data Acquisition:

  • Perform untargeted LC-MS/MS analysis on natural product extracts. Use data-dependent acquisition (DDA) or iterative MS/MS to fragment precursor ions.
  • Convert raw data to an open format (e.g., .mzML) using tools like MSConvert (ProteoWizard).

• Spectral Processing and Database Curation:

  • Process data with feature detection tools (e.g., MZmine, XCMS) to align chromatographic peaks and assemble MS1 and MS2 spectra.
  • Prepare a local database of natural product structures in SMILES format from sources like PubChem, COCONUT, and NPAtlas [28].
  • Optionally, generate in-silico variant libraries by applying biochemical transformation rules (e.g., methylation, hydroxylation, glycosylation) to the core database structures.

• VInSMoC Database Search:

  • Input the experimental MS/MS spectral file (.mzML or .mgf) and the molecular structure database (.smi or similar) into the VInSMoC tool [28].
  • Configure search parameters:
    • Set precursor mass tolerance.
    • Define the variable modification space (e.g., allow for additions/subtractions of common biochemical units).
    • Set the statistical significance threshold (p-value or E-value).
  • Execute the search. VInSMoC works by estimating statistical significance for matches between spectra and molecular structures, even when they are not exact matches, thereby reducing false identifications [28].

• Analysis of Results and Putative Pathway Mapping:

  • Review the output, which will include both exact matches and putative variant matches.
  • For high-scoring variant hits, analyze the proposed chemical difference (e.g., an extra hydroxyl group). Manually inspect the spectral match to verify key fragment ions are explained.
  • To contextualize findings, use genomic data if available. For a microbial extract, screen its genome for biosynthetic gene clusters (BGCs) using tools like antiSMASH [28]. Cross-reference the proposed variant structure with the predicted enzymatic capabilities of the BGC to assess biosynthetic plausibility.

• Validation: Prioritize high-confidence variant hits for isolation (e.g., using preparative HPLC) and subsequent definitive structure elucidation by NMR, following the protocol in Application Note 1.

The Scientist's Toolkit for MS-Based CASE:

• High-Resolution LC-MS/MS System: For acquiring accurate mass and fragmentation data.
• Data Processing Suite (e.g., MZmine, XCMS, Progenesis QI): For feature detection and spectral alignment.
• Public Molecular Databases (GNPS, PubChem, COCONUT, NPAtlas): Sources of structural and spectral data for dereplication [28].
• VInSMoC Software: The core algorithm for variant-tolerant spectral database search [28].
• Genomics Analysis Pipeline (e.g., antiSMASH): For linking chemical variants to biosynthetic pathways [28].

The workflow for this MS-centric approach is depicted in the following diagram.

Integrated Strategies and Future Perspectives

The most powerful applications of CASE involve the strategic integration of NMR and MS data. An integrated workflow might begin with LC-MS/MS analysis and VInSMoC screening to rapidly dereplicate and highlight novel variants. Subsequently, promising, non-dereplicated variants are purified, and their structures are conclusively elucidated using the detailed, NMR-based CASE protocol. This synergy maximizes throughput while ensuring definitive structural proof.

Future advancements in CASE will be driven by deeper integration of machine learning for spectral prediction and scoring, the expansion of open, high-quality spectral databases, and the development of unified platforms that seamlessly combine NMR, MS, and genomic data [50] [28]. For researchers, adopting these protocols mitigates the risk of miselucidation and transforms the structure determination process from a bottleneck into a robust, data-driven engine for natural product discovery and drug development [37].

The structure elucidation of natural products, particularly novel or trace compounds, remains a formidable analytical challenge. Traditional reliance on nuclear magnetic resonance (NMR) spectroscopy, while powerful, can be impeded by factors such as limited sample quantity, compound complexity, or the presence of stereochemical ambiguities. Within this context, Computer-Assisted Structure Elucidation (CASE) has emerged as a critical framework, systematically leveraging computational power to interpret spectroscopic data and generate structurally consistent candidates [37]. The evolution of CASE now extends beyond the traditional core of 1D/2D NMR to embrace a holistic, multi-technique integration.

This article details the advanced integration of Mass Spectrometry (MS), Infrared Spectroscopy (IR), and Density Functional Theory (DFT) calculations into the CASE workflow. This paradigm shift is driven by the need to solve structures from sub-microgram quantities of material, where classical isolation and extensive NMR study are impossible [51]. MS provides molecular formula and fragment-based structural clues, IR delivers definitive functional group and bonding information, and DFT calculations serve as a powerful validator, predicting spectroscopic properties for candidate structures with high accuracy. Together, they form a complementary triad that can deliver unambiguous structural identification, as exemplified by the discovery of the salinilactones, a new class of volatile bicyclic lactones from marine bacteria [51]. This integrated approach minimizes the risk of misassignment—a persistent issue in natural products research [37]—and streamlines the path from detection to confident identification.

Core Methodology: Principles of MS, IR, and DFT Integration

The synergy between MS, IR, and DFT is rooted in the orthogonal information each technique provides. The workflow is iterative and hypothesis-driven, moving from broad characterization to precise validation.

• Mass Spectrometry (MS) for Molecular Blueprinting: High-resolution mass spectrometry (HR-MS) is the entry point, delivering the exact molecular formula (e.g., C₁₀H₁₄O₃) and calculating the number of double bond equivalents (DBEs) [51]. Tandem MS (MS/MS) fragmentation patterns reveal skeletal features and key functional groups. For instance, characteristic ions from McLafferty rearrangements can indicate specific acyl side chains [51].

• Infrared (IR) Spectroscopy for Functional Group Interrogation: Solid-phase Gas Chromatography/IR (GC/IR) provides high-sensitivity, chromatographically-resolved IR spectra [51]. This yields direct evidence of functional groups through their signature stretching vibrations (e.g., carbonyls at ~1700-1800 cm⁻¹). Critically, IR can confirm or rule out structural features suggested by MS, such as distinguishing between ester and ketone carbonyls or confirming the absence of C=C double bonds [51].

• Density Functional Theory (DFT) for In Silico Validation: DFT calculations bridge the gap between proposed chemical structures and observed experimental data. For a set of candidate structures consistent with MS and IR data, DFT is used to:

  • Optimize Geometry: Find the lowest-energy three-dimensional conformation of each candidate.
  • Calculate Vibrational Frequencies: Simulate the IR spectrum for each optimized structure.
  • Compare and Rank: Perform a computational comparison (e.g., using derivative correlation algorithms) between the calculated and experimental IR spectra to identify the best match [51]. This quantitative ranking objectively prioritizes candidates for synthetic verification.

Case Study Application: Elucidating the Salinilactones

The power of this integrated approach is demonstrated in the identification of salinilactones A–C from Salinispora bacteria [51]. With only nanogram quantities available, a traditional structure elucidation was not feasible.

1. Initial Profiling (GC/MS & GC/IR): Analysis of bacterial volatiles revealed three unknown compounds with shared characteristic MS fragments at m/z 140 and 122, suggesting a common core [51]. HR-MS established the molecular formula for the major component (Salinilactone B) as C₁₀H₁₄O₃ (4 DBEs). GC/IR showed two distinct carbonyl stretches: a ketone (1696 cm⁻¹) and a high-energy ester (1769 cm⁻¹), the latter indicating ring strain. No C=C stretch was observed [51].

2. Hypothesis Generation & DFT Screening: The data (4 DBEs, two carbonyls, no alkenes) pointed to a bicyclic lactone system. Several isomeric [3.1.0]-bicyclic structures were plausible [51]. Instead of synthesizing all possibilities, dispersion-corrected DFT calculations (B3LYP-D/6-311G(p,d)) were employed to simulate the IR spectra of the candidate isomers. Visual and algorithmic comparison of the calculated versus experimental IR fingerprint regions unambiguously identified the correct structure [51].

3. Synthesis and Absolute Configuration: The DFT-prioritized structure was successfully synthesized, and its MS, IR, and chromatographic properties matched the natural product [51]. Asymmetric synthesis and chiral GC/MS further determined the absolute configuration of the natural enantiomer as (1R,5S) [51].

Table 1: Summary of Analytical Data for Salinilactone B Elucidation [51]

Analytical Technique	Key Data Obtained	Structural Information Revealed
GC/HR-MS	m/z 182.0972 [M]⁺	Molecular Formula: C₁₀H₁₄O₃
MS Fragmentation	m/z 140, 122, 153, 125	C5 acyl side-chain; fragmentation pattern consistent with γ-lactone core.
Solid-Phase GC/IR	Stretches at 1769 cm⁻¹ and 1696 cm⁻¹	Presence of a strained ester and a ketone carbonyl group.
	No absorption ~1620-1680 cm⁻¹	Absence of carbon-carbon double bonds.
DFT Calculation (B3LYP-D)	Simulated IR spectrum of candidate 8	Best match to experimental IR spectrum; structure selected for synthesis.

Research Reagent Solutions & Essential Materials

Table 2: Key Research Reagents and Materials for Integrated MS/IR/DFT Analysis

Item	Function in Analysis	Specific Application Notes
Closed-Loop Stripping (CLSA) Apparatus	Pre-concentrates trace volatile organic compounds from liquid or air samples for analysis.	Essential for preparing samples from bacterial cultures or environmental sources for GC-based techniques [51].
Solid-Phase GC/IR Interface	Enables high-sensitivity, flow-through IR detection of GC eluents. Provides chromatographically resolved IR spectra.	Critical for obtaining functional group data on nanogram-scale components in complex mixtures [51].
Deuterated Internal Standards (e.g., d₂-8)	Used in isotope dilution methods for absolute quantification of target analytes in complex matrices.	Allows accurate measurement of natural product titers directly from culture extracts [51].
DFT Software with Dispersion Correction	Performs quantum mechanical calculations to optimize molecular geometry and predict spectroscopic properties.	Functionals like B3LYP-D and basis sets like 6-311G(p,d) are recommended for accurate IR simulation [51]. Modern GPU-accelerated cloud platforms can drastically reduce computation time [52].
Chiral GC Stationary Phases	Enables separation of enantiomers by gas chromatography.	Required for determining the absolute configuration of chiral natural products by comparison with synthesized enantiomers [51].

Detailed Experimental Protocols

Protocol 5.1: Sample Preparation and Data Acquisition for Trace Volatiles

Application: Profiling volatile organic compounds from microbial cultures. Steps:

• Culture Extraction: Grow bacterial culture (e.g., Salinispora arenicola) in appropriate liquid medium. Use Closed-Loop Stripping Analysis (CLSA): sparge the culture headspace with a controlled flow of purified air or nitrogen, trapping volatiles on a porous polymer filter (e.g., Tenax TA) [51].
• Thermal Desorption: Transfer the trapped filter to a thermal desorption unit connected to the GC system. Desorb volatiles with a rapid heat pulse (e.g., 250°C for 5 min) onto the GC column using a cryogenic or focusing trap.
• Hyphenated GC/MS/IR Analysis:
  • GC Conditions: Use a high-resolution capillary column (e.g., 5% phenyl polysiloxane, 30m x 0.25mm). Employ a temperature gradient (e.g., 40°C hold, then 10°C/min to 300°C).
  • MS Detection: Use electron ionization (EI) at 70 eV. Acquire full-scan mass spectra (e.g., m/z 40-400). For HR-MS, use appropriate calibration for mass accuracy (< 5 ppm).
  • IR Detection: Direct the GC effluent post-MS (via a split) to a solid-phase IR detector. Co-add IR scans across the chromatographic peak to obtain a clean vibrational spectrum [51].

Protocol 5.2: DFT-Assisted IR Spectrum Matching and Candidate Ranking

Application: Selecting the most probable structure from a set of isomers. Steps:

• Candidate Enumeration: Based on molecular formula, MS fragments, and IR functional groups, draw all plausible constitutional isomers.
• Conformational Search: For each isomer, perform a thorough conformational search using a molecular mechanics force field (e.g., MMFF) to identify low-energy conformers [51].
• Quantum Chemical Optimization: Subject the lowest-energy conformer(s) to geometry optimization using DFT. A common and reliable level of theory is B3LYP with an empirical dispersion correction (B3LYP-D) and a polarized triple-zeta basis set (e.g., 6-311G(p,d)) [51].
• Frequency Calculation: At the optimized geometry, run a frequency calculation. This yields the harmonic vibrational frequencies and intensities. Apply a standard scaling factor (e.g., 0.97) to the calculated frequencies to account for known systematic errors.
• Spectral Comparison & Ranking: Import the scaled, calculated IR spectrum and the experimental GC/IR spectrum into analysis software (e.g., GRAMS). Use a derivative correlation algorithm to compute a similarity score (correlation coefficient) between the calculated and experimental spectra across the fingerprint region (e.g., 1500-800 cm⁻¹) [51]. Rank candidates by this score.

Protocol 5.3: Validation via Synthesis and Chiral Analysis

Application: Confirming identity and determining absolute configuration. Steps:

• Targeted Synthesis: Design and execute a synthetic route for the top-ranked DFT candidate. For racemic synthesis, plan a route that constructs the core skeleton efficiently [51].
• Analytical Comparison: Analyze the synthetic product using the same GC/MS and GC/IR conditions as the natural product. Confirm identity by perfect co-elution and matching of all mass spectral fragments and IR absorption bands.
• Asymmetric Synthesis & Configuration Assignment: Develop an asymmetric synthesis to obtain a non-racemic sample of the target compound. Use a chiral catalyst in the key step (e.g., asymmetric copper-catalyzed cyclopropanation) [51].
• Chiral GC/MS Analysis: Analyze the natural product, the racemic synthetic standard, and the enantiomerically enriched synthetic product on a chiral GC column. The elution order establishes the absolute configuration of the natural enantiomer [51].

Workflow and Relationship Visualizations

Integrated MS/IR/DFT Workflow for CASE

Proposed Biosynthetic Relationship of Salinilactones [51]

Overcoming Challenges: Troubleshooting and Optimizing CASE for Complex Natural Products

The structure elucidation of natural products is a foundational pillar of modern drug discovery. However, this process encounters significant bottlenecks when dealing with proton-deficient scaffolds—complex molecular frameworks with low hydrogen density—and truly novel chemotypes for which no close structural analogs exist. These challenges are amplified within the context of Computer-Assisted Structure Elucidation (CASE), where algorithmic logic depends heavily on the quality, quantity, and interpretability of input spectroscopic data [23] [53].

Proton-deficient compounds, such as highly conjugated polyketides, glycosylated flavonoids with many quaternary carbons, or novel aromatic heterocycles, yield information-sparse 1H NMR spectra. The scarcity of protons reduces the number of observable through-bond correlations (e.g., in COSY and TOCSY experiments) and weakens key proton-detected heteronuclear signals (e.g., in HMBC spectra), creating an underdetermined system for structural assembly [54] [53]. Simultaneously, ambiguity arises from dynamic phenomena common in natural products, including tautomerism, intramolecular hydrogen bonding, and pH-dependent protonation states, which can lead to variable or averaged spectroscopic signals that misrepresent the true, bioactive structure [54] [55].

This article details practical protocols and strategies to manage these data limitations. It frames the discussion within the broader thesis that advancing CASE requires not just more powerful algorithms, but a refined, synergistic workflow integrating targeted experimental spectroscopy, intelligent data pre-processing, and computational modeling to resolve ambiguity where traditional data falls short.

Core Strategies and Detailed Protocols

The following strategies are designed to systematically overcome the informational gaps presented by challenging scaffolds.

Protocol: Enhanced NMR Data Acquisition and Interpretation for Proton-Deficient Systems

When standard 1H-13C correlation experiments are insufficient, a targeted suite of advanced NMR experiments is required.

1. Prioritize Long-Range Heteronuclear Correlation Experiments:

• Experiment: 1,n-ADEQUATE. This experiment, although suffering from low sensitivity due to its reliance on 1JCC coupling, provides direct carbon-carbon connectivity information. This is invaluable for establishing the skeleton of a proton-deficient molecule independent of proton networks.
• Application Note: For a novel bis-thiazole-quinoxaline hybrid scaffold with multiple quaternary carbons [56], standard HMBC may not unambiguously connect fragments across carbonyl or heteroatom bridges. 1,n-ADEQUATE can directly link the quinoxaline carbons to the thiazole ring system.

2. Utilize Deuterium Isotope Effects to Probe Exchangeable Protons and Tautomerism:

• Procedure: Acquire standard 13C NMR spectra in a protonated solvent (e.g., DMSO-d6). Then, add a drop of D2O, shake, and re-acquire the 13C spectrum. Measure the isotope-induced chemical shift changes (nΔC(2H)) for all carbon resonances [54].
• Interpretation: Significant isotope shifts (>> 0.1 ppm) on carbon atoms 2-4 bonds away from an exchangeable site (OH, NH) indicate the presence of a strong, persistent intramolecular hydrogen bond. This can "lock" a conformational or tautomeric state. For example, this technique is critical for defining the dominant tautomer of curcumin or usnic acid in solution, which dictates its bioactive conformation [54].

3. Implement Quantitative 2J/3J Coupling Constant Analysis:

• Experiment: 1H-13C HMBC with high digital resolution in the F1 (13C) dimension or specialized pure-shift HMBC experiments.
• Application: For tautomeric equilibria involving an OH group (e.g., β-diketones), the magnitude of the two-bond coupling constant (2JCH) between the carbonyl carbon and the proton of the enolic OH is a direct probe. A measurable 2JCH (~3-6 Hz) confirms the enol form's contribution to the equilibrium [54].

Table 1: Advanced NMR Experiments for Resolving Structural Ambiguity

Experiment	Key Information	Target Use Case	Limitation
1,n-ADEQUATE	Direct C-C connectivity via 1JCC [54].	Tracing backbone in polycycles with quaternary carbons.	Very low sensitivity; requires high sample concentration.
HMBC with D2O	Identifies correlations to exchangeable protons; clarifies ambiguous long-range peaks.	Confirming N/O-methylation sites or involvement of NH/OH in hydrogen bonds.	Signal loss for protons exchanged with deuterium.
Deuterium Isotope Effect	Detects hydrogen bonding and maps electron density changes upon H/D exchange [54].	Determining tautomeric states, strength of intramolecular H-bonds.	Requires careful measurement of small Δδ (ppb).
HSQC-TOCSY	Extends connectivity from a directly bonded 1H-13C pair to the entire proton spin system.	Unraveling overlapped sugar or amino acid spin systems in proton-rich regions.	Limited utility in proton-deficient core regions.

Protocol: Optimizing CASE System Input with Ambiguous and Fragment Data

The performance of a CASE system is directly contingent on the constraints provided. Strategic input is crucial for novel scaffolds [53].

1. Curating the "Fuzzy" Constraint List from Ambiguous HMBC Data:

• Procedure: Do not discard weak or ambiguous HMBC cross-peaks. Instead, log them as "fuzzy" or "range" constraints. For example, if a proton shows a weak correlation to a cluster of 3-4 carbons in a congested spectral region (e.g., 120-130 ppm), input this as a possible long-range connection to any of those carbons, rather than forcing a single choice.
• Rationale: This prevents the CASE engine from prematurely excluding the correct structure. The system's probabilistic ranking algorithms (e.g., DP4, CFIT) will later use calculated chemical shifts to evaluate which candidate best satisfies all constraints, including the ambiguous ones [53].

2. Defining "Must-Have" and "Unknown" Fragments from Prior Knowledge:

• Procedure: If partial structure information is available (e.g., a sugar moiety identified by COSY/TOCSY, or a common natural product substructure from literature), define it as a "must-have" fragment. For novel hybrid scaffolds synthesized from known precursors [56], input the identifiable cores (e.g., quinoxaline, thiazole) as fixed fragments. Mark the connecting linker regions and unknown substituents as fully variable.
• Outcome: This dramatically reduces the combinatorial space the structure generator must explore, focusing computational resources on elucidating only the truly unknown portions of the molecule [53].

Table 2: Performance Metrics of a CASE System (ACD/Structure Elucidator) in Blind Trials [53]

Trial Category	Number of Challenges	Success Rate	Average Processing & Elucidation Time	Key Challenge Types
Double/Single Agreement	100	89%	~84 minutes	Large heteroatom count, symmetry, poor data.
Double-Blind Trials	10	Not separately quantified	Similar to average	Complete unknown to both submitter and analyst.
Incorrect/Rejected	12	-	-	Data inadequate (poor S/N, impurities) or molecular size >1000 Da (in early software versions).

Computational Integration and Specialized Toolkits

The Integrated AI-CASE Pipeline for Novel Scaffold Prediction

Artificial Intelligence, particularly deep neural networks (DNNs), is moving beyond dereplication to actively assist in the elucidation of novel scaffolds [24].

Strategy: Multi-Model Consensus Prediction.

• Feature Extraction: Transform raw or pre-processed spectroscopic data (NMR chemical shifts, MS/MS fragmentation patterns) into a unified feature vector.
• Parallel Model Inference: Feed the features into specialized AI models:
  • A Generative Chemical Language Model trained on known natural product scaffolds (e.g., from the CAS Content Collection [24]) proposes plausible core structures.
  • A 3D Pharmacophore Matching DNN [24] predicts potential biological targets, which can be used as a filter for generated candidates.
  • A Spectra Prediction Model (e.g., a neural network predicting 13C NMR shifts) scores and ranks the candidates generated by the CASE engine.
• Consensus Ranking: Aggregate the scores from the AI models with the CASE system's own probabilistic score (e.g., average chemical shift deviation) to produce a final, robust candidate ranking.

Protocol: In Silico Modeling of Dynamic Protonation and Tautomerism

For scaffolds where biological activity depends on a specific protonation state or tautomeric form, static structure elucidation is insufficient [55].

Procedure: MD-Guided Determination of Bioactive Conformers.

• Initial Structure(s): Use the CASE-elucidated structure as a starting point.
• Protonation State Sampling: Using software like Schrödinger's Epik or MOE's Protonate3D, generate all plausible protonation states and tautomeric forms for the molecule at physiological pH (7.4). This is critical for molecules with multiple basic nitrogens or acidic phenols, like many alkaloids [55].
• Molecular Dynamics (MD) Simulation: Solvate each major protonation state/tautomer in an explicit water box and run a short, unrestrained MD simulation (50-100 ns).
• Analysis of Stability: Analyze the trajectories for stability of key intramolecular hydrogen bonds observed via NMR isotope effects [54]. The dominant, persistent conformation from the most stable protonation state is the most likely bioactive form.
• Validation via Docking: Dock each stable conformer into a known or predicted protein target. The form yielding the most favorable binding energy and complementary interactions provides a final, functional validation of the elucidated structure [55].

The Scientist's Toolkit: Essential Reagents and Software

Table 3: Research Toolkit for Managing Data Ambiguity

Category	Item/Resource	Function	Example/Note
Chemical Reagents	Deuterium Oxide (D2O)	Identifies exchangeable protons, clarifies spectra, measures isotope effects [41] [54].	Add a drop to NMR sample to exchange OH/NH; suppresses water peak.
	Chiral Solvating Agents (e.g., Pirkle's alcohol)	Helps assign absolute configuration or resolve enantiomeric signals in novel scaffolds.	Useful when novel scaffold contains a newly formed chiral center.
NMR & Data Software	ACD/Structure Elucidator	Commercial CASE suite for structure generation from spectral constraints [53].	Used in blind trials; averages ~84 min. for elucidation [53].
	MestReNova, TopSpin	Standard for NMR processing, peak picking, and preparing data for CASE input.	Accurate peak picking is critical for generating reliable constraints.
	Gaussian, ORCA	Software for Density Functional Theory (DFT) calculations of NMR chemical shifts.	Final validation by comparing calculated vs. experimental 13C shifts [54].
Modeling & AI Software	Schrödinger Suite, MOE	For MD simulations, protonation state sampling, and docking studies [55].	Determines bioactive protonation state and conformation.
	Specialized AI Models	DNNs for target prediction, spectra prediction, and generative scaffold design [24].	Emerging tools to guide CASE and predict activity early.
Databases	CAS Content Collection, NuBBE	Curated databases for dereplication and training AI models [24].	Essential to avoid rediscovering known compounds.

Effectively managing insufficient and ambiguous data in the elucidation of proton-deficient and novel scaffolds requires a paradigm shift from linear analysis to an integrated, cyclical workflow. The future of CASE in natural products research lies in tighter, real-time feedback loops between experiment and computation. Promising directions include the development of CASE systems that actively suggest the next most informative NMR experiment to run based on preliminary data analysis, and the broader integration of X-ray crystallographic data or cryo-EM density maps of natural product-protein complexes as direct structural constraints. Furthermore, as AI models trained on expansive, curated datasets like the CAS Content Collection become more sophisticated [24], their role will evolve from passive assistants to proactive partners in proposing structurally novel, yet biophysically plausible, scaffolds that push the boundaries of natural product-inspired drug discovery.

Addressing 'Non-Standard' Correlations and Fuzzy Structure Generation in 2D NMR Data

Application Notes: The Central Role of CASE in Modern Natural Products Research

Within the broader thesis of Computer-Assisted Structure Elucidation (CASE) for natural products research, the expert interpretation of 2D NMR data stands as the critical, rate-limiting step. The isolation of novel bioactive compounds from nature routinely yields complex molecular architectures whose structural puzzles cannot be solved by library matching. Here, CASE systems function as indispensable force multipliers, transforming spectral data into definitive chemical structures with high efficiency and reliability [57]. The core challenge—and the focus of this document—is managing the uncertainty inherent in spectral interpretation, particularly 'non-standard' NMR correlations and incomplete structural constraints.

Modern CASE expert systems, such as the widely cited Structure Elucidator (StrucEluc), are engineered to overcome the historical bottleneck of the "combinatorial explosion." For a typical natural product molecular formula, the number of possible structural isomers can be on the order of Avogadro's number (10²³) [57]. Traditional approaches are computationally intractable. CASE systems invert this problem by using spectroscopic data as a filter. They generate a constrained set of candidate structures that satisfy all experimental observations, most importantly the network of through-bond correlations from experiments like COSY, HSQC, and HMBC [57] [58].

The most significant advancement in this field has been the shift from 1D to 2D NMR data as the primary input. While 1D spectra provide chemical shifts, 2D correlations map the molecular skeleton's connectivity. However, a persistent complication is the presence of 'non-standard' correlations—typically HMBC correlations that span more than three bonds ('long-range') or appear with unexpected intensity. These can arise from special conformational or electronic effects and, if misidentified as standard two- or three-bond correlations, will lead the elucidation to an impasse or an incorrect structure [57] [59].

To address this, contemporary CASE methodologies incorporate Fuzzy Structure Generation (FSG). FSG algorithms do not require the spectroscopist to know a priori which correlations are non-standard or their exact length [57] [58]. Instead, the system allows for a user-defined degree of "fuzziness" in the connectivity data. It then performs structure generation under the hypothesis that a certain number of correlations in the input table may be non-standard, efficiently navigating the vast structural space to identify all plausible candidates that satisfy both the definite and the ambiguous constraints. This approach has proven decisive in the structure revision of natural products, where published data often contains such ambiguities [58].

The following table summarizes quantitative performance data from recent applications, illustrating the efficiency and resolving power of CASE methodologies.

Table 1: Performance Metrics of CASE in Representative Structure Revisions and Elucidations [58] [31]

Compound / System	Key Challenge	CASE Method Applied	Structures Generated (k)	Time to Solution	Key Metric / Outcome
Macahydantoin B (Revision)	Ambiguous HMBC correlations; published structure incorrect.	Fuzzy Structure Generation (FSG), limiting non-std. correlations to 1.	1,987 → 124 ranked	6 seconds	Correct structure identified as top rank (DP4A probability >99.99%).
Clionastatin A (Verification)	Complex polyhalogenated steroid; need for structural validation.	Molecular Connectivity Diagram (MCD) analysis & exhaustive structure generation.	Not Specified	Minutes	Confirmed published structure as unique solution fitting all NMR data.
Traditional CASE Workflow (Theoretical)	Navigating combinatorial isomer space for a ~50-atom molecule.	Fragment-based generation, spectral filtering.	10¹⁰ – 10²⁰ (theoretical isomer space)	Minutes to Hours	Drastically reduces candidates to a manageable ranked list.
CLAMS AI Model (Benchmark)	Accelerating elucidation for small organic molecules.	Transformer-based encoder-decoder (spectra-to-SMILES).	Direct generation (no combinatorial list)	Seconds	Top-15 accuracy of 83% for molecules ≤ 29 atoms.

Core Protocol: Managing Ambiguity with Fuzzy Structure Generation

This protocol details the process for using Fuzzy Structure Generation within a CASE system to elucidate a novel natural product when the 2D NMR data contains ambiguous or suspected non-standard correlations.

Preprocessing and Data Input

• Determine Molecular Formula: Obtain an accurate molecular formula from high-resolution mass spectrometry (HR-MS) data. This is the non-negotiable foundation for all subsequent structure generation [57].
• Prepare 1D NMR Data Tables: Create tables of observed ¹H and ¹³C chemical shifts. Assign each signal a unique label (e.g., H-1, C-1). At this stage, assignments are provisional.
• Prepare 2D NMR Correlation Tables: For each 2D experiment (HSQC, HMBC, COSY/TOCSY), create a table listing all observed correlations.
  • HSQC: List direct ¹H-¹³C one-bond connectivities.
  • HMBC: List all observed long-range ¹H-¹³C correlations. Crucially, do not pre-judge their bond length. Input all correlations with a standard length setting (typically 2-3 bonds), but flag any that appear unusually intense or are suspected to be longer-range based on tentative structure hypotheses.
  • COSY/TOCSY: List ¹H-¹H through-bond couplings.
• Define Optional Constraints: Input any prior knowledge (e.g., presence of a pre-identified functional group from IR, isolated moieties from degradation experiments, biosynthetic reasoning) as "must-have" fragments or "forbidden" substructures [58].

Creating and Editing the Molecular Connectivity Diagram (MCD)

• Generate Initial MCD: The CASE software will automatically generate an MCD—a visual graph where atoms (nodes) are connected by bonds (edges) as dictated by the input correlation tables [58] [31].
• Analyze for Contradictions: The system will highlight conflicting connectivities (e.g., a carbon atom proposed to have more than four bonds) that signal the presence of non-standard correlations.
• Set Atom Properties: Manually define properties (e.g., hybridization, possible heteroatom membership, membership in a specific fragment) based on chemical shift predictions and prior knowledge to further constrain the solution space [58].

Executing Fuzzy Structure Generation

• Initiate FSG: Select the Fuzzy Structure Generation algorithm. The system will prompt for the maximum number of non-standard correlations (n) to be considered [58].
• Set the Fuzziness Parameter (n): If the MCD shows minor contradictions, start with n=1 or 2. For highly complex or messy spectra, a higher n may be required. The goal is to find the smallest n that yields chemically plausible structures.
• Run Structure Generation: The system will iteratively generate structures under the assumption that n correlations from the input list are allowed to be of non-standard length. It uses efficient graph theory algorithms to assemble structures from fragments and free atoms that satisfy all other definite constraints [57].
• Filter and Rank Results: The output is a file containing all valid structural isomers. The system then filters duplicates and ranks the candidates. Ranking is typically based on the goodness-of-fit between the candidate's predicted NMR chemical shifts (using empirical methods like HOSE codes or neural networks) and the experimental shifts [58].

Result Validation and Selection

• Analyze Top-Ranked Candidates: Examine the top 5-10 structures. The correct structure is typically, but not always, the highest-ranked.
• Employ DFT Validation: For final confirmation, especially if the top candidates are close in ranking, calculate the ¹³C NMR chemical shifts of each candidate using Density Functional Theory (DFT). Compare calculated vs. experimental shifts using statistical probability measures (e.g., DP4, CP3). The candidate with the highest probability (e.g., >99%) can be confidently assigned [58].
• Check Consistency: Verify that the selected structure logically explains all observed correlations, including the identified non-standard ones (e.g., through-space interactions, four-bond couplings in conjugated systems).

Diagram 1: Fuzzy Structure Generation (FSG) Workflow for CASE (77 characters)

The AI Evolution: Integrating Knowledge Graphs and Generative Models

The future of CASE lies in transcending purely logic- and rule-based systems through deep integration with Artificial Intelligence (AI). The next evolution is moving from assisted elucidation to anticipatory science, where AI models mimic the holistic reasoning of an expert natural products chemist [60].

1. Generative AI Models: New architectures, such as transformer-based encoder-decoder models, are challenging the traditional CASE workflow. Systems like CLAMS treat spectroscopic data (IR, UV, 1H NMR concatenated as a 1D array) as an input "language" and directly generate the molecular structure (as a SMILES string) as an output "translation" [31]. This end-to-end approach bypasses the combinatorial generation and filtering steps, offering solution times of seconds with promising accuracy for small molecules. While currently best-suited for less complex structures, this represents a paradigm shift towards direct spectral interpretation by AI.

2. Knowledge Graphs for Causal Inference: A major limitation in applying AI to natural products is the fragmented, multimodal nature of the data (genomics, metabolomics, bioassay results, published literature). The proposed solution is the construction of a federated Natural Product Science Knowledge Graph [60]. In this graph, nodes represent entities (e.g., a compound, a spectral peak, a gene cluster, a biological activity), and edges represent relationships (e.g., "compound A produces spectrum B," "gene cluster X biosynthesizes compound Y"). Such a structure organizes disconnected data into a machine-readable web of knowledge.

An AI model trained on this knowledge graph can perform causal inference, not just pattern recognition. For example, it could anticipate the structure of an unknown metabolite by reasoning across linked nodes: from a microbial genome (node) to a predicted biosynthetic gene cluster (edge) to a known chemical scaffold (node) to a predicted mass fragmentation pattern (edge) [60]. This mimics the logical deduction of a human expert connecting disparate pieces of evidence.

Diagram 2: Integrated AI and CASE Framework (68 characters)

Table 2: Key Research Reagent Solutions for Advanced CASE Workflows

Tool / Reagent Category	Specific Examples	Function in CASE Workflow
Expert System Software	ACD/Structure Elucidator Suite [61] [58], StrucEluc [57]	Core platform for data input, MCD creation, fuzzy structure generation, and candidate ranking via empirical NMR prediction.
Quantum Chemical Computation Software	Gaussian, ORCA, CFOUR	Performs DFT calculations to predict highly accurate NMR chemical shifts and coupling constants for final structure validation and stereochemistry assignment [58].
Spectral Databases & Predictors	ACD/NMR Predictors [59], CSEARCH [59], PubChem [61]	Provides libraries of known spectra for dereplication and robust algorithms (HOSE, neural networks) for rapid chemical shift prediction of candidate structures.
Generative AI & ML Tools	CLAMS-like transformer models [31], DP4-AI [31]	Offers alternative, ultra-fast elucidation pathways for suitable molecules and provides statistical probability measures for candidate selection.
Knowledge Graph Resources	LOTUS Initiative (Wikidata) [60], Experimental NP Knowledge Graph (ENPKG) [60]	Federated, structured databases connecting chemical, biological, and spectroscopic data to fuel next-generation, anticipatory AI models.
qNMR Standards	High-purity internal standards (e.g., maleic acid, 1,4-bis(trimethylsilyl)benzene) [62]	Enables quantitative NMR for assessing sample purity, crucial for obtaining accurate integral and intensity data for structure generation.

Within the framework of computer-assisted structure elucidation (CASE) for natural products, the adage "garbage in, garbage out" is profoundly consequential. The successful identification of novel bioactive compounds hinges on the integrity of the spectral data input and the chemical relevance of the fragment libraries used for reasoning [34] [26]. This document details essential protocols and application notes to optimize the preparatory stages of the CASE workflow. Focusing on rigorous peak picking, robust data processing, and the strategic use of natural product-derived fragment libraries, these practices are designed to enhance the accuracy, efficiency, and reliability of de novo structure elucidation in natural products research and drug discovery [63] [64].

Foundational Principles and Quantitative Benchmarks

Effective CASE application requires an understanding of the quantitative landscape of natural product space and the performance characteristics of constituent fragment libraries. The following tables summarize key chemoinformatic data essential for library design and evaluation.

Table 1: Structural and Diversity Metrics of Representative Natural Product Fragment Libraries

Library / Source	Avg. Molecular Weight (Da)	Avg. Heavy Atoms	Avg. Fsp³	Key Structural Features	Pairwise Similarity (Tanimoto, Morgan2)	Reference
Colombian NP (NPDBEjeCol)	~234	~17	High	Rich in oxygen, low N, tetrahydropyran rings, phenolic moieties	Highest uniqueness; minimal fragment overlap with other sets [65]	[65]
COCONUT (Full Database)	Not specified	Not specified	High	Maximum chemical diversity from >400,000 NPs	Low median similarity, indicating high diversity [66]	[66]
Pseudo-NP (Designed)	150-300	Variable	Very High (Fsp³ >0.45 target)	3D-shaped, chiral centers, combined biosynthetic scaffolds	Occupies novel, inaccessible regions of NP chemical space [63]	[63]
FDA-Approved Drugs	358-386	26-28	Moderate	Higher nitrogen & sulfur content, more aromatic rings	Distinct from NP-derived libraries in chemical space [65]	[65]
Rule-of-Three Compliant	≤300	≤22	Variable	clogP ≤3, HBD ≤3, HBA ≤3, PSA ≤60 Ų	Optimized for solubility and ligand efficiency in biophysical assays [67]	[67]

Table 2: Input Data Quality Tolerances for CASE System Performance

Parameter	Optimal Specification	Acceptable Range	Impact on CASE Output	Corrective Action
¹H NMR Resolution	< 1 Hz	1-2 Hz	Broad peaks lead to missed correlations; inaccurate coupling constants.	Optimize shimming, sample prep, use higher field instrument.
¹³C NMR Signal-to-Noise (S/N)	> 100:1	> 50:1	Low S/N causes erroneous peak picking and missing quaternary carbons.	Increase scan count, concentrate sample, use cryoprobe.
HSQC/HMBC Peak Picking Accuracy	> 98% correct correlations	> 90%	Incorrect peaks generate invalid structural constraints, causing combinatorial explosion or wrong solution [34].	Manual verification, use of adaptive thresholding algorithms.
Molecular Formula Accuracy (HR-MS)	< 3 ppm error	< 5 ppm error	Incorrect formula leads to generation of impossible structures.	Calibrate instrument with standard; use internal reference.
Number of HMBC Correlations per Carbon	≥ 2-3	≥ 1	Insufficient long-range constraints fail to define molecular connectivity [26].	Acquire with longer mixing time (e.g., 8-10 Hz JCH).

Application Notes & Detailed Protocols

Protocol for Optimized Peak Picking and Spectral Processing for 2D NMR Data

Objective: To extract accurate and complete atom-atom correlation lists from 2D NMR spectra (HSQC, HMBC, COSY) for input into a CASE system [34] [26].

Materials:

• Raw NMR data (FID files)
• Processing software (e.g., MestReNova, TopSpin, ACD/Spectrus)
• CASE software (e.g., ACD/Structure Elucidator, CISOC-SES)

Procedure:

• Initial Processing:

  • Apply careful apodization (e.g., sine-bell or QSINE) to balance resolution and S/N.
  • Perform Fourier transformation and phase correction meticulously to obtain a flat baseline.
  • For ¹H dimensions, reference to residual solvent peak. For ¹³C dimensions, calibrate indirectly via the known HSQC correlation of the solvent.

• Automated Peak Picking with Adaptive Parameters:

  • HSQC: Use a high sensitivity threshold initially to capture all potential correlations. Set the peak width parameters based on the ¹H spectral resolution. A post-picking filter to remove singlets or peaks with implausible ¹³C chemical shifts (e.g., < 0 ppm or > 220 ppm) is recommended.
  • HMBC: Employ a lower threshold than HSQC due to lower intensity. Use a J_CH coupling constant range of 6-10 Hz for optimization. Critical Step: Implement a "connectivity check" against the HSQC peak list. Any HMBC peak must have at least one coordinate (¹H or ¹³C) matching an atom in the HSQC list; unflagged outliers often represent noise [26].

• Manual Curation and Validation:

  • Visually inspect the spectrum overlay with picked peaks. Manually add weak but genuine correlations missed by automation, particularly key long-range HMBC correlations to quaternary carbons.
  • Delete obvious noise peaks (isolated, non-symmetrical).
  • Consistency Check: For each protonated carbon (HSQC peak), verify the presence of at least one corresponding COSY or TOCSY correlation and one HMBC correlation. Flag any atom lacking these for further investigation [34].

• Data Export:

  • Export the finalized peak lists as a table with columns for: Spectrum Type, Atom 1 (¹H δ), Atom 2 (¹³C δ for HSQC/HMBC; ¹H δ for COSY), Correlation Intensity, and J-coupling (if known).
  • Import this table along with the molecular formula into the CASE system.

Protocol for Generating and Utilizing a Focused Natural Product Fragment Library

Objective: To create a targeted fragment library from natural products for use in CASE to seed structure generation or validate candidate structures [63] [65] [66].

Materials:

• Natural product database (e.g., COCONUT, Dictionary of Natural Products, or a proprietary in-house collection)
• Cheminformatics toolkit (e.g., RDKit, Open Babel)
• RECAP (Retrosynthetic Combinatorial Analysis Procedure) or similar fragmentation algorithm [66] [67].
• CASE software with fragment library import functionality [34].

Procedure:

• Database Curation:

  • Standardize molecular structures: remove salts, neutralize charges, generate canonical tautomers.
  • Apply fragment-like filters: Molecular Weight (100 ≤ MW ≤ 300 Da), heavy atom count (≤22), and Rule-of-Three compliance (clogP ≤3, HBD/HBA ≤3) to define the "fragment-sized" subset [63] [67].
  • Calculate 3D-descriptors (e.g., Principal Moment of Inertia ratio, Plane of Best Fit) or Fsp³ to select for non-planar, shapely fragments (Fsp³ > 0.45) [63].

• In Silico Fragmentation:

  • Apply the RECAP algorithm, which cleaves molecules at synthetically relevant bonds (e.g., amide, ester, amine, ether linkages) [66].
  • Retain all unique terminal fragments.
  • Optional Diversity Selection: Cluster fragments using molecular fingerprints (e.g., ECFP4). Select a representative subset from each cluster to maximize scaffold diversity while minimizing library size.

• Library Annotation & Formatting:

  • Annotate each fragment with its source natural product(s) and the chemotype of the cleavage site.
  • Calculate and store predicted ¹³C NMR chemical shifts for each fragment using incremental or HOSE code-based methods.
  • Format the library in the required input for the target CASE system (e.g., SD file with custom properties).

• Integration with CASE Workflow:

  • As a Search Database: Use the library to dereplicate common natural product scaffolds. The CASE system can search its internal fragment database for partial matches to the experimental data [34].
  • As a Constraint: Manually define a suspected fragment from the library as an "obligatory" substructure within the Molecular Connectivity Diagram (MCD). This dramatically reduces the number of structures generated [34] [26].
  • For Post-Generation Ranking: Compare candidate structures generated by the CASE system against the fragment library. Candidates containing fragments prevalent in natural products may be assigned a higher likelihood score.

Visual Workflows and Pathways

Diagram 1: CASE Workflow with Critical Input Optimization Stages

Diagram 2: Natural Product Fragment Library Generation and Application Pipeline

Diagram 3: Data Quality and Consistency Validation Logic Pathway

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Reagents, Software, and Databases for CASE-Driven Natural Products Research

Item / Resource	Category	Primary Function in CASE Context	Key Features / Notes	Reference / Source
ACD/Structure Elucidator Suite	CASE Software	Core platform for de novo structure generation from spectral data.	Generates Molecular Connectivity Diagrams (MCD), ranks candidates via DP4/ deviation, integrates fragment libraries, processes vendor-agnostic data [34].	Commercial Software [34]
CISOC-SES	CASE Expert System	Automated structure elucidation and resonance assignment.	Demonstrated application to complex NPs (e.g., betulinic acid); uses spectral data independent of background info [64].	Academic/Research Software [64]
RDKit with MolVS	Cheminformatics Toolkit	Data curation, standardization, and fragmentation (RECAP).	Open-source. Essential for curating NP databases and generating standardized fragment libraries [66].	Open-Source Library
COCONUT (COlleCtion of Open NatUral producTs)	NP Database	Source of >400,000 compounds for fragment library generation.	Largest open-access NP database; provides immense scaffold diversity for fragment mining [66].	Public Database [66]
Dictionary of Natural Products (DNP)	NP Database	Source of curated, characterized natural products.	Contains fragment-sized NPs; used for analysis of 3D shape and sp3 richness [63].	Commercial Database
SPiDER Software	Target Prediction	Predicts biological targets for fragment-sized natural products.	Guides hypothesis generation by linking NP fragments to potential protein targets (e.g., sparteine to opioid receptor) [63].	Commercial/Research Software [63]
Deuterated NMR Solvents (e.g., DMSO-d6, CD3OD)	Research Reagent	Provide stable, non-interfering medium for NMR analysis.	Essential for obtaining high-resolution, reproducible 2D NMR spectra. Low water content is critical.	Commercial Supplier
RECAP Algorithm	Computational Method	Retrosynthetically cleaves molecules into sensible fragments.	Creates "privileged" fragments with synthetic handles; standard for NP fragment library creation [66] [67].	Algorithm [66]
TMAP Visualization	Chemoinformatics Tool	Visualizes high-dimensional chemical space of large fragment libraries.	Enables intuitive analysis of library diversity and overlap between NP and drug-like spaces [66].	Open-Source Algorithm [66]

Combining Multimodal Spectroscopic Data (e.g., NMR and IR) to Resolve Ambiguous Isomers

The structure elucidation of complex natural products and ambiguous isomers remains a significant challenge in drug discovery. This article details application protocols for integrating Nuclear Magnetic Resonance (NMR) and Infrared (IR) spectroscopic data within a Computer-Assisted Structure Elucidation (CASE) framework. The described methodologies leverage multivariate data analysis and emerging machine learning techniques trained on multimodal datasets to resolve structural ambiguities that single spectroscopic techniques cannot. This integrated approach provides a more robust, efficient, and less error-prone pathway for determining novel molecular structures in natural products research [37] [3].

The history of structure elucidation is marked by technological revolutions, from crystallography to NMR spectroscopy [68]. Despite advancements, a "disturbing number" of incorrect natural product structures are still reported, often due to interpretive errors from incomplete or ambiguous data [37]. Computer-Assisted Structure Elucidation (CASE) systems were developed to address this by generating all possible structures consistent with spectroscopic data and ranking them by probability [37] [3]. While powerful, traditional CASE programs primarily rely on 2D NMR data (COSY, HMBC) and can struggle with isomers that produce near-identical NMR correlations or contain uncommon structural motifs [37].

Multimodal integration, particularly of NMR and IR data, offers a compelling solution. NMR excels at mapping carbon-hydrogen frameworks and connectivity, while IR spectroscopy provides sensitive, complementary information on functional groups and bond vibrations. Combining these orthogonal data streams provides a more complete structural fingerprint. As experts note, just as analysts use multiple techniques simultaneously, machine learning models trained on combined FT-IR and NMR data outperform those using a single spectral type for functional group identification [69]. This synergy is now being propelled by the availability of large-scale synthetic spectral datasets and advanced multivariate analysis software, moving multimodal CASE from a specialized concept to a practical, high-throughput protocol [70] [71].

Application Protocols and Workflows

Protocol 1: Integrated NMR-IR Data Acquisition and Preprocessing for CASE

This protocol standardizes the acquisition and preparation of NMR and IR data for integrated CASE analysis, ensuring consistency and machine-readability.

• Sample Preparation: Prepare a purified sample (1-5 mg) of the unknown natural product. For NMR, dissolve in 0.6 mL of deuterated solvent (e.g., CDCl₃, DMSO-d₆). For IR, prepare a KBr pellet or a thin film from a volatile solvent.
• Spectroscopic Acquisition:
  • 1D and 2D NMR: Acquire ¹H, ¹³C, DEPT-135, COSY, and HSQC/HMBC spectra on a spectrometer (≥400 MHz recommended). Calibrate spectra to solvent residual peaks. Key parameters include sufficient digital resolution and transients for adequate signal-to-noise in 2D experiments [37].
  • FT-IR Spectroscopy: Acquire a mid-IR spectrum (400–4000 cm⁻¹) with 2–4 cm⁻¹ resolution. Collect a minimum of 32 scans to reduce noise. Record a background spectrum under identical conditions.
• Data Preprocessing:
  • NMR Data: Process spectra (Fourier transformation, phase correction, baseline correction). Convert peak lists (chemical shifts, coupling constants, correlation peaks) into a standardized format (e.g., JCAMP-DX, MOLFILE) for CASE software import [3].
  • IR Data: Convert transmittance to absorbance. Apply vector normalization (e.g., Min-Max) to account for path length differences. Optionally, use advanced corrections like Extended Multiplicative Signal Correction (EMSC) to remove physical light-scattering effects [72].
• Data Fusion for Analysis: Align the processed spectral datasets to the same sample identifier. For computational analysis, represent the IR spectrum as a 1D vector of absorbance values at fixed wavenumber intervals (e.g., every 3-4 cm⁻¹, resulting in ~1100 data points). This creates a unified digital fingerprint of the sample [69].

Protocol 2: Computational Generation of a Reference NMR-IR Dataset for Machine Learning

This protocol outlines steps for generating high-quality synthetic spectral data to train or benchmark machine learning models for structure elucidation, based on methods used to create large public datasets [70].

• Molecular Curation:
  • Source a diverse set of organic molecule structures (e.g., from USPTO, ChEMBL, or ZINC databases) relevant to natural product space (heavy atom count: 5-35) [70].
  • Filter SMILES strings to contain common organic elements (C, H, N, O, S, P, halogens). Use cheminformatics toolkits (e.g., RDKit) to generate 3D coordinates and check for errors [70].
• Conformational Sampling via Molecular Dynamics (MD):
  • Parameterize molecules using a force field like GAFF2. For each molecule, run a classical MD simulation in vacuum (e.g., 100-200 ps at 300 K) using software like LAMMPS or GROMACS to sample thermally accessible conformations [70].
  • Extract thousands of molecular snapshots (conformations) from the equilibrated trajectory for subsequent quantum mechanical (QM) calculation.
• Quantum Mechanical Spectral Prediction:
  • IR Spectra (Anharmonic): For a subset of MD snapshots, compute the dipole moment autocorrelation function from ab initio MD (e.g., using Density Functional Theory - DFT). This captures anharmonic effects missing in simple harmonic calculations, yielding more realistic IR line shapes [70]. Alternatively, train a machine learning potential (e.g., a Deep Neural Network) on DFT-derived dipole moments to predict spectra for all snapshots rapidly [70].
  • NMR Chemical Shifts: Perform single-point DFT calculations (e.g., using Gaussian, ORCA, or CPMD with functionals like PBE or WP04) on snapshots to compute isotropic magnetic shielding tensors for ¹H and ¹³C nuclei. Convert shielding values to chemical shifts using a reference standard [70].
• Dataset Assembly and Validation:
  • Average spectral predictions over all snapshots for each molecule to produce a single, thermally averaged IR spectrum and a set of NMR chemical shifts with possible conformational variance.
  • Package the data (molecule identifier, SMILES, IR vector, NMR shift lists) in an open format (e.g., JSON, HDF5). Validate accuracy against a hold-out set of experimental spectra from public databases [70].

Protocol 3: Multimodal Machine Learning for Functional Group Identification and Isomer Ranking

This protocol describes using a pre-trained multimodal artificial neural network (ANN) to analyze experimental spectra of an unknown compound, providing functional group predictions and isomer discrimination.

• Model Input Preparation:
  • Process the experimental IR and NMR spectra as described in Protocol 1, Step 4.
  • For ¹H and ¹³C NMR spectra, perform data binning to reduce dimensionality and sparsity. Divide the ¹H NMR range (0-12 ppm) into 12 bins of 1 ppm and the ¹³C NMR range (0-220 ppm) into 44 bins of 5 ppm. Encode each bin as '1' if a peak is present or '0' if absent [69].
  • Concatenate the processed data into a single input vector: [IR_Absorbance_1, ..., IR_Absorbance_N, ¹H_Bin_1, ..., ¹H_Bin_12, ¹³C_Bin_1, ..., ¹³C_Bin_44].
• Model Inference:
  • Input the fused spectral vector into a trained multimodal ANN. The model used in recent research featured an input layer matching the vector size, two hidden layers (e.g., with 256 and 128 nodes, ReLU activation), and an output layer with 17 nodes (sigmoid activation) corresponding to common functional groups [69].
  • The model outputs a probability (0 to 1) for the presence of each functional group (e.g., alcohol, ketone, aromatic, amine).
• Interpretation and CASE Integration:
  • Use the functional group predictions as soft constraints within a traditional CASE system (e.g., ACD/Structure Elucidator, SENECA). This reduces the combinatorial space of candidate structures the CASE software must generate [37] [3].
  • Alternatively, to rank a small set of candidate isomers (e.g., from a CASE output), compute the spectral similarity between the experimental input and the predicted spectra for each candidate (available from a dataset like Protocol 2's output). The candidate with the smallest multimodal distance (e.g., Euclidean distance in the fused spectral vector space) is the most probable structure.

Diagram 1: Workflow for Multimodal Spectroscopy-Enhanced CASE

Performance Metrics and Data

The quantitative advantage of integrating multimodal spectroscopic data is evident in the performance of machine learning models.

Table 1: Performance Comparison of Spectroscopy Models for Functional Group Identification [69]

Model Input Data	Macro-Average F1 Score	Key Advantage	Primary Limitation
FT-IR Only	0.88	Excellent for specific bonds (C=O, O-H)	Overlap in fingerprint region; silent modes
¹³C NMR Only	0.85	Direct carbon environment information	Low sensitivity; no proton info
¹H NMR Only	0.79	High sensitivity; connectivity via J-coupling	Complex multiplet patterns; signal overlap
Multimodal (IR + ¹H + ¹³C NMR)	0.93	Complementary data resolves ambiguities; robust to weak signals	Requires all three datasets; more complex preprocessing

Table 2: Key Specifications of the IR-NMR Multimodal Computational Dataset [70]

Feature	Specification
Total Molecules	177,461 (IR), 1,255 (NMR subset)
Molecular Source	USPTO Patent Database
Heavy Atom Range	5 to 35
IR Spectral Type	Anharmonic, from MD dipole autocorrelation
NMR Data	¹H & ¹³C chemical shifts from DFT calculations
Computational Method	Hybrid MD-DFT with ML acceleration (DeepPot-SE)
Primary Use Case	Training/benchmarking ML models for joint spectral interpretation
Data Accessibility	Open access via Zenodo

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Essential Tools for Multimodal Spectroscopic Analysis

Category	Item / Software	Primary Function in Protocol
Computational Chemistry & MD	GAFF2 Force Field [70]	Provides parameters for classical molecular dynamics simulations of organic molecules.
	LAMMPS [70]	High-performance molecular dynamics simulator used to generate conformational trajectories.
	CPMD / Gaussian / ORCA [70]	Software for Density Functional Theory (DFT) calculations to predict NMR chemical shifts and accurate dipole moments for IR.
Cheminformatics & ML	RDKit [70]	Open-source toolkit for cheminformatics; used to process SMILES, generate 3D coordinates, and handle molecule I/O.
	DeePMD-kit [70]	Implements the Deep Potential framework for training machine learning potentials to accelerate spectral calculations.
	Python (SciKit-Learn, PyTorch/TensorFlow)	Ecosystem for building, training, and deploying the multimodal artificial neural network (ANN) models.
Data Analysis & CASE	SIMCA [71]	Multivariate Data Analysis (MVDA) software for advanced spectral preprocessing, PCA, PLS, and classification.
	ACD/Structure Elucidator [3]	A leading CASE expert system that uses spectroscopic data to generate and rank plausible chemical structures.
Reference Databases	USPTO-Spectra Dataset [70]	A large-scale synthetic dataset of anharmonic IR and NMR spectra for training multimodal ML models.
	NIST Chemistry WebBook [69]	Public repository of experimental IR spectra used for model training and validation.
	SDBS (Spectral Database) [69]	Public repository of experimental NMR spectra used for model training and validation.

Diagram 2: Computational Pipeline for Synthetic Spectral Data Generation

The integration of multimodal spectroscopic data, particularly NMR and IR, within a CASE framework represents a powerful evolution in structure elucidation. Protocols that leverage multivariate analysis of fused spectral data and machine learning models trained on large-scale computational datasets provide a systematic approach to resolving ambiguous isomers. This not only increases accuracy but also accelerates the discovery pipeline for natural products and pharmaceuticals.

The future of this field is closely tied to advances in artificial intelligence and data availability. The development of large, high-quality multimodal spectral datasets will enable the training of more sophisticated "foundation models" for spectroscopy [70]. Furthermore, the direct integration of these AI tools into commercial CASE software will make multimodal analysis a routine, accessible step for researchers. As these tools evolve, the vision of a fully automated, highly reliable analytical workflow for determining complex natural product structures moves closer to reality, promising to unlock novel chemical space for drug discovery [37] [3].

Algorithm Selection and Parameter Tuning for Large or Structurally Dense Molecules

The structure elucidation of natural products represents a persistent bottleneck in drug discovery. These molecules, often large, structurally dense, and rich in stereogenic centers, defy analysis by conventional methods. Computer-Assisted Structure Elucidation (CASE) systems have emerged as transformative tools, shifting the paradigm from manual, expertise-dependent interpretation to automated, data-driven deduction [58]. The core challenge in applying CASE to complex natural products lies in the strategic selection of algorithms and the precise tuning of their parameters to navigate the vast, combinatorial chemical space efficiently and accurately.

Traditional exhaustive structure generation approaches become computationally intractable for molecules with more than 30 heavy atoms or intricate polycyclic systems. Consequently, modern protocols increasingly rely on hybrid strategies that integrate physics-based spectral matching, fragment-based assembly, and machine learning optimization [73]. The performance of these algorithms is highly sensitive to input parameters, where suboptimal choices can lead to failed elucidations, erroneous structures, or prohibitive computational cost. This document provides detailed application notes and protocols for algorithm selection and parameter tuning, framed within a practical workflow for the CASE-driven analysis of large or structurally dense natural products.

Algorithmic Approaches for Complex Molecules

Selecting the appropriate algorithm is the first critical step. The choice depends on the primary data available, the suspected molecular complexity, and the computational resources at hand.

Spectral Matching and Fragment-Based Assembly

For problems where Nuclear Magnetic Resonance (NMR) data is the primary source, algorithms that bypass exhaustive generation are essential. The NMR-Solver framework exemplifies this approach, integrating large-scale spectral database matching with physics-guided, fragment-based optimization [73]. It uses a two-stage process: first, retrieving candidate molecular fragments from a spectral database using deep learning-based similarity search; second, assembling these fragments into complete candidate structures through a Markov Chain Monte Carlo (MCMC) process guided by a scoring function that combines spectral match quality and molecular stability.

Performance Data: Benchmarking on experimental datasets shows NMR-Solver can identify correct structures for molecules with 20-35 heavy atoms, where traditional CASE might generate millions of candidates. Its fragment-based approach reduces the candidate pool by several orders of magnitude prior to final ranking [73].

Evolutionary Algorithms for Chemical Space Exploration

When the problem involves searching ultra-large, make-on-demand chemical libraries (e.g., billions of compounds) for hits against a biological target, exhaustive docking is impossible. Evolutionary algorithms (EAs) like REvoLd are designed for this task [74]. They treat molecules as individuals in a population, with fitness defined by a scoring function (e.g., docking score). Through iterative cycles of selection, crossover (fragment exchange), and mutation (fragment replacement), the population evolves toward higher fitness.

Key Hyperparameter Insights from REvoLd: [74]

• Population Size: A starting population of 200 molecules provides sufficient diversity without excessive initial computational cost.
• Generations: Optimization for 30 generations typically balances convergence and exploration; significant hits often appear by generation 15.
• Selection Pressure: Allowing the top 50 individuals to propagate maintains high fitness while preserving diversity. Incorporating crossovers between fit individuals and targeted mutations to low-similarity fragments prevents premature convergence to local minima.

Table 1: Comparative Performance of Algorithmic Approaches for Large Molecules

Algorithm Type	Primary Data Input	Best For	Key Strength	Computational Scale	Reported Enrichment/Accuracy
Fragment-Based (NMR-Solver) [73]	¹H/¹³C NMR Spectra	Structure elucidation of novel, medium-large organics	Integrates physical principles & database learning; highly interpretable	Minutes to hours on GPU	>80% top-1 accuracy on experimental benchmarks
Evolutionary (REvoLd) [74]	3D Protein Target, Fragment Libraries	Screening ultra-large combinatorial libraries	Efficient exploration of billion+ molecule spaces with flexible docking	Thousands of docking calc. vs. billions	869x to 1622x hit rate enrichment over random
In-Context LLM (LICO) [75]	Molecular String (SMILES), Property Labels	Black-box molecular optimization tasks	Generalizes to new objectives with minimal examples	Fast inference after training	State-of-the-art on PMO-1K low-budget benchmark
Bayesian-Optimized Clustering (DBOpt) [76]	Spatial Coordinates (e.g., SMLM data)	Analyzing nanoscale molecular organization in imaging	Unbiased, automated parameter selection for density-based clustering	Efficient validation for large point clouds	Accurately recovers feature sizes in 2D/3D experimental data

Neural Network Potentials and In-Context Learning

For steps requiring high-accuracy quantum mechanical calculations, such as final structure ranking or geometry optimization, Neural Network Potentials (NNPs) trained on massive datasets like OMol25 offer DFT-level accuracy at a fraction of the computational cost [77]. These models, such as the eSEN and UMA architectures, are essential for making quantum chemical validation feasible for large molecules.

For generative tasks or property prediction, Large Language Model (LLM)-based approaches like LICO demonstrate strong in-context learning [75]. LICO can be adapted to optimize diverse molecular properties by learning from a small set of example input-output pairs provided in its prompt, making it flexible for scenarios with limited labeled data.

Parameter Tuning Methodologies

Algorithm performance is critically dependent on parameters. Manual tuning is inefficient and non-reproducible. The following automated methodologies are recommended.

Bayesian Optimization for Clustering Parameters

In analyses like single-molecule localization microscopy (SMLM), defining clusters of molecular localizations is key. Density-based algorithms like DBSCAN require parameters (eps, min_samples) that are non-intuitive to set. The DBOpt protocol uses an efficient Density-based Cluster Validation (DBCV) score as an internal validation metric and couples it with Bayesian optimization to find the parameter set that maximizes this score [76].

Protocol: DBOpt for SMLM Data Clustering [76]

• Input: 2D or 3D spatial coordinates of localizations.
• Define Bounds: Set plausible search bounds for eps (e.g., 1-100 nm) and min_samples (e.g., 5-50).
• Optimization Loop: Run Bayesian optimization for ~50 iterations. In each iteration:
  • A parameter set is proposed.
  • Clustering is executed.
  • The DBCV score is computed (measuring intra-cluster density vs. inter-cluster separation).
• Output: The parameter set yielding the maximum DBCV score is selected. Convergence is confirmed by multiple random restarts.

Hyperparameter Tuning for Evolutionary Algorithms

Tuning EAs requires balancing exploration and exploitation. The REvoLd study established a protocol based on iterative testing on a pre-scored subset of the chemical space [74].

Protocol: Tuning an EA for Library Screening

• Create a Benchmark Subset: Dock a random sample (e.g., 1 million molecules) from the full library to create a proxy for the fitness landscape.
• Iterative Testing: Systematically test combinations of:
  • Selection mechanisms (e.g., tournament, rank-based).
  • Mutation and crossover rates.
  • Operators that introduce "large jumps" (e.g., swapping to low-similarity fragments).
• Evaluate: Use the hit rate (number of molecules found above a docking score threshold) and diversity of discovered scaffolds as primary metrics.
• Finalize Protocol: Adopt the hyperparameter set that produces stable enrichment and diverse hits over multiple runs.

Table 2: Parameter Tuning Methods and Key Metrics

Tuning Method	Best Applied To	Core Principle	Evaluation Metric	Advantage
Bayesian Optimization with DBCV [76]	Density-based clustering parameters (e.g., DBSCAN)	Maximize an internal validity index (DBCV) via surrogate model	DBCV Score (-1 to 1)	Fully automated; unbiased; requires no ground truth
Iterative Benchmarking on Subset [74]	Evolutionary algorithm operators & rates	Empirical performance testing on a manageable representative sample	Hit Rate, Scaffold Diversity	Pragmatic; directly optimizes for application-specific outcomes
Grid/ Random Search	Generic algorithm hyperparameters	Systematic or random sampling of parameter space	Any relevant performance score (e.g., accuracy, loss)	Simple to implement; embarrassingly parallel
In-Context Learning [75]	Adapting LLMs to new tasks	Providing task examples within the model's prompt	Task-specific performance (e.g., property value)	Requires no retraining; highly flexible for new problems

Diagram 1: DBOpt Workflow for Automated Parameter Tuning (Max Width: 760px)

Integrated Protocols for CASE Application

This section outlines a step-by-step protocol for applying the discussed algorithms to revise or elucidate the structure of a complex natural product.

Protocol for Preventive Structure Revision Using CASE/DFT

This protocol, based on the analysis of 12 real revision cases [58], aims to verify or correct a proposed structure before costly total synthesis.

Materials & Input Data: Published (^1)H and (^{13})C NMR chemical shifts, HSQC, HMBC, and COSY correlations (even if incomplete or containing errors); Molecular formula from HRMS. Workflow:

• Data Preparation & MCD Creation: Input all spectral data into a CASE system (e.g., ACD/Structure Elucidator). The software generates a Molecular Connectivity Diagram (MCD), a graph representing all possible atom connections consistent with the data.
• Structure Generation: Initiate the Fuzzy Structure Generation (FSG) algorithm. This algorithm efficiently generates all structural isomers within the given constraints, allowing for possible misassignments in the input data. Typical run time for molecules with ~30 atoms is seconds to minutes [58].
• Filtering & Ranking: The generated structures (often thousands) are filtered and ranked based on the accuracy of predicted vs. experimental (^{13})C NMR shifts using empirical methods (e.g., HOSE codes, neural networks).
• Quantum Mechanical Validation: For the top 5-10 ranked candidates, perform Density Functional Theory (DFT) calculations (e.g., ωB97X-D/6-311+G(d,p)) to compute more accurate chemical shifts and DP4 probability scores. The candidate with the highest DP4 probability is the correct structure.
• Result: A revised structure with high statistical confidence, potentially avoiding months of synthetic work.

Diagram 2: CASE/DFT Structure Revision Workflow (Max Width: 760px)

Protocol for De Novo Elucidation of Large Molecules

For a completely unknown, complex molecule, an integrated protocol combining several algorithms is necessary.

Workflow:

• Initial Processing: Use NMR-Solver or a similar fragment-based algorithm as the primary engine. Input 1D and 2D NMR spectra. The algorithm will output a ranked list of most probable complete structures [73].
• Candidate Validation: Subject the top 3-5 candidates from Step 1 to high-level DFT or NNP geometry optimization and NMR calculation. Use the OMol25-derived NNPs for large systems where DFT is prohibitive [77]. Compute DP4 or related probabilities.
• Stereochemistry Determination: For candidates with multiple stereocenters, use Bayesian optimization to tune parameters for conformational search algorithms, ensuring comprehensive coverage of the conformational space before calculating NMR properties for each diastereomer.
• Final Assignment: The structure with the highest composite score (agreement with experimental NMR, MS fragmentation, and any available ECD/VCD data) is assigned.

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Essential Computational Tools for CASE of Complex Molecules

Tool / Resource	Type	Primary Function in CASE	Key Feature for Large Molecules	Reference/Example
ACD/Structure Elucidator	Commercial Software Suite	Core CASE platform for structure generation & ranking	Fuzzy Structure Generation handles inconsistent data; handles large structure sets.	[58]
NMR-Solver	AI/Physics Framework	Automated structure elucidation from NMR spectra	Fragment-based assembly avoids combinatorial explosion.	[73]
Gaussian, ORCA, PSI4	Quantum Chemistry Packages	DFT NMR/ECD calculations for final validation	Can be coupled with NNPs for faster geometry steps.	Standard Tools
Meta OMol25 NNPs (eSEN, UMA)	Pre-trained Neural Potentials	High-accuracy energy/force calculations	Near-DFT accuracy for biomolecules & metal complexes at low cost.	[77]
Rosetta (REvoLd)	Molecular Modeling Suite	Evolutionary screening of ultra-large libraries	Efficiently searches billion-molecule spaces with flexible docking.	[74]
DBOpt Implementation	Clustering Optimization Script	Automated parameter tuning for DBSCAN/HDBSCAN	Enables robust analysis of SMLM data from complex cellular structures.	[76]
HMDB, NP-MRD, LOTUS	Public Spectral & Natural Product DBs	Reference data for spectral matching & dereplication	Critical for fragment retrieval and prior knowledge integration.	[60]

The future of CASE for natural products lies in moving beyond isolated algorithms toward integrated, reasoning systems. The next paradigm involves constructing a Natural Product Science Knowledge Graph [60]. This graph would connect nodes representing chemical structures, spectroscopic data, genomic biosynthetic gene clusters, and biological activity through defined relationships. Algorithm selection and parameter tuning would then be guided by this global knowledge structure. For instance, an AI model could recommend an NMR-Solver-first protocol for a molecule whose partial structure appears linked to a specific biosynthetic pathway in the graph, or suggest starting parameters for an EA based on the historical performance of similar scaffolds. This shift from manual protocol design to AI-assisted, knowledge-informed workflow generation will be essential for tackling the growing complexity of natural product discovery and fully realizing the potential of computer-assisted structure elucidation.

Ensuring Accuracy: Validation Strategies and Comparative Analysis of Modern CASE Systems

The structural elucidation of novel natural products represents a fundamental challenge in drug discovery and chemical research. Despite advanced spectroscopic techniques, misassignments—particularly of complex stereochemistry—persist as a significant problem, undermining downstream research and development efforts [78]. Within this context, Computer-Assisted Structure Elucidation (CASE) systems have emerged as indispensable tools, leveraging algorithms to generate all possible structural candidates consistent with experimental spectroscopic data [4] [26].

However, the output of a CASE system is typically a ranked list of plausible isomers. The final, critical step is validation: determining which candidate structure is correct with the highest possible confidence. Traditional metrics like the correlation coefficient (R²) or Mean Absolute Error (MAE) between calculated and experimental Nuclear Magnetic Resonance (NMR) chemical shifts have limitations, as they do not provide a statistical measure of probability and can be skewed by systematic errors [78].

This is where advanced validation metrics, specifically DP4/DP4* probabilities and Chemical Shift Deviation (CSD) scores, become paramount. These metrics transform qualitative assessment into a quantitative, statistically robust decision-making process. DP4* (an enhanced version of the original DP4) employs Bayesian analysis to compute the probability that each candidate structure is the correct one, based on the distribution of errors between density functional theory (DFT)-calculated and experimental NMR shifts [78]. When integrated into the CASE workflow, these metrics provide a powerful, objective filter, drastically reducing the risk of publication and propagation of incorrect structures.

Core Validation Metrics: Principles and Quantitative Performance

DP4 and DP4* Probability Analysis

The DP4 method, introduced by Goodman and later refined to DP4+ (or DP4), is a Bayesian probability tool designed to assign the most likely structure among a set of candidates [78]. Its core innovation is treating the discrepancies between DFT-calculated and experimental chemical shifts as arising from a known probability distribution (a *t-distribution).

• Key Evolution from DP4 to DP4: The original DP4 used scaled chemical shift data, forcing the mean error (μ) to zero. DP4 incorporates both scaled (sDP4+) and unscaled (uDP4+) data, separating nuclei by hybridization state (sp, sp2, sp3). This accounts for systematic errors more effectively and significantly improves stereochemical discrimination, especially for complex natural products [78].
• Mathematical Basis: The probability P(i) for candidate i is calculated using Bayes' theorem, based on the fit of its calculated shifts to the experimental data, relative to all other candidates. It requires pre-determined parameters (μ, σ, ν) that describe the error distribution for different atom types and calculation levels [78].
• Performance Data: A critical review covering 2015-2020 documented the application of DP4+ in determining the structures of over 200 natural products [78]. Its success spans a wide range of molecular sizes and complexities. A landmark study on the flexible cyclic peptide cyclocinamide A demonstrated DP4's power, where it correctly identified the true stereoisomer (4S,7R,11R,14S) with a 73.2% probability, a prediction later confirmed by total synthesis [79]. Key performance data is summarized in Table 1.

Chemical Shift Deviation (CSD) and Root-Mean-Square Deviation (RMSD) Scores

While DP4 provides a probabilistic ranking, CSD and RMSD scores offer complementary, intuitive measures of the absolute agreement between calculation and experiment.

• Definition and Calculation:
  • Mean Absolute Error (MAE): The average absolute difference between calculated and experimental shifts for all nuclei. Sensitive to outliers.
  • Root-Mean-Square Deviation (RMSD): The square root of the average of squared differences. More heavily penalizes large errors.
  • Corrected MAE (CMAE): An MAE value corrected for systematic linear deviation via a scaling procedure.
• Role in Validation: These metrics provide a quick, preliminary check of data quality. A candidate with an anomalously high MAE/RMSD is likely incorrect. However, they are not probabilistic and should not be used in isolation for final decision-making among closely ranked candidates [78].

Table 1: Performance Summary of DP4/DP4* and Traditional Metrics

Metric	Statistical Basis	Key Output	Typical Threshold for Confidence	Primary Advantage	Primary Limitation
DP4*	Bayesian probability (t-distribution)	Probability (0-100%) that a candidate is correct.	>95% considered highly confident; >99% is definitive [78].	Provides objective statistical probability; excellent for stereochemistry.	Requires accurate DFT calculations; performance depends on correct parameter set.
MAE	Descriptive statistics	Average error in ppm.	Varies by nucleus: ~0.1-0.3 ppm for ¹H; ~1-3 ppm for ¹³C.	Simple, intuitive measure of average agreement.	Non-probabilistic; can be deceptively low for wrong structures with scaled data.
RMSD	Descriptive statistics	Root-mean-square error in ppm.	Slightly higher than MAE; similar variability.	Penalizes large outliers more than MAE.	Non-probabilistic; cannot statistically distinguish between candidates.
CMAE	Descriptive statistics (scaled)	Scaled average error in ppm.	Similar to MAE but more consistent across calculation levels.	Removes systematic linear bias from calculations.	Still a non-probabilistic measure.

Integrated Protocol: Applying DP4* and CSD within a CASE Workflow

The following protocol outlines the steps for employing these validation metrics, from CASE system output to final structural assignment.

Phase 1: Candidate Generation via CASE

• Data Input: Feed processed 1D and 2D NMR data (¹H, ¹³C, HSQC, HMBC, COSY), the molecular formula (from HRMS), and any prior knowledge (e.g., known fragments) into the CASE software (e.g., ACD/Structure Elucidator, Sherlock) [34] [30].
• Structure Generation: The CASE system generates all constitutional isomers consistent with the data. For stereochemistry, it may output a set of relative or absolute stereoisomers for a given connectivity.
• Initial Ranking: The CASE software provides an initial ranking, often based on internal consistency checks or simple deviation scores.

Phase 2: In-depth Validation with DFT and DP4*

• Candidate Selection: Select the top 5-10 ranked constitutional isomers from the CASE output. For each, enumerate all relevant stereoisomers.
• Conformational Search & Averaging:
  • Perform a thorough conformational search for each candidate using molecular mechanics (e.g., MMFF).
  • Optimize all low-energy conformers (typically within a 3-5 kcal/mol window) using DFT (e.g., B3LYP/6-31G*).
  • Calculate their Gibbs free energies at a higher theory level to determine Boltzmann populations.
• NMR Calculation & Scaling:
  • Calculate the NMR shielding tensors for each populated conformer using a higher-level DFT method (e.g., mPW1PW91/6-311+G(2d,p) or similar) [78] [79].
  • Compute the Boltzmann-averaged chemical shifts.
  • (Optional but recommended): Apply a linear regression scaling procedure to the calculated vs. experimental shifts to correct for systematic error.
• Probability & Deviation Calculation:
  • Input the experimental and averaged calculated shifts (both scaled and unscaled) into the DP4* Excel spreadsheet or script [78].
  • Calculate the DP4* probability for each candidate isomer.
  • Simultaneously, compute the MAE and RMSD.
• Decision Making:
  • The candidate with the highest DP4* probability (ideally >95-99%) is assigned as the correct structure.
  • Corroborate this result by checking that the same candidate also has reasonable MAE/RMSD values consistent with the level of theory used.
  • In cases where the top two probabilities are close (e.g., 55% vs. 45%), the result is inconclusive. Return to Phase 1 to check data quality, consider additional spectroscopic constraints, or employ higher-level DFT calculations.

Diagram 1: Integrated CASE and DP4 Validation Workflow. This chart outlines the process from raw data to a validated structure, highlighting the critical, iterative role of DP4* probability analysis [78] [4] [34].

Diagram 2: DP4* Probability Calculation Logic. This diagram illustrates the core algorithmic steps within the DP4* calculation, showing how data categorization and Bayesian inference produce the final probability score [78].

Successfully implementing this protocol requires access to specific computational tools and databases.

Table 2: Research Reagent Solutions for CASE & DP4 Validation

Tool/Resource Category	Specific Example(s)	Function & Role in Validation
Commercial CASE Suite	ACD/Structure Elucidator [34]	Industry-standard platform for automated structure generation from NMR/MS data. Provides initial candidate list and can integrate DP4 scoring.
Open-Source CASE System	Sherlock [30]	Free, modular system for peak picking, constraint management, and structure generation. Enables transparent workflow.
Quantum Chemistry Software	Gaussian, ORCA, Spartan	Performs the essential DFT calculations for geometry optimization and NMR chemical shift prediction at high levels of theory.
DP4 Calculation Interface	DP4+ Excel Spreadsheet [78]	User-friendly tool to input calculated and experimental shifts and automatically compute DP4* probabilities.
Conformational Search Software	CONFLEX, MacroModel, RDKit	Systematically explores the conformational landscape of flexible molecules to ensure accurate Boltzmann-averaged NMR predictions.
Chemical Shift Database	NMRShiftDB, SDBS	Public repositories of experimental NMR data used for dereplication and as benchmarks for calculated shifts.

Within the modern framework of computer-assisted structure elucidation for natural products, validation is not a final step but an integral, guiding principle. The synergistic use of DP4/DP4* probability analysis and Chemical Shift Deviation scores provides a rigorous, quantitative foundation for moving from a list of computational possibilities to a single, high-confidence structural assignment. As documented in numerous case studies, including complex peptides like cyclocinamide A [79], this methodology significantly reduces the rate of structural misassignment. Its integration into commercial and open-source CASE platforms [34] [30] marks a critical advancement towards more reliable, efficient, and data-driven natural product discovery, directly supporting the development of new therapeutic agents.

The structural elucidation of novel natural products represents a central, yet often bottleneck, challenge in drug discovery and chemical ecology. Traditional manual interpretation of Nuclear Magnetic Resonance (NMR) spectra is a time-intensive process heavily reliant on expert knowledge. Computer-Assisted Structure Elucidation (CASE) systems have emerged as transformative tools, leveraging algorithms to propose and rank candidate structures from spectroscopic data, thereby enhancing the speed, objectivity, and reliability of the process [3]. Within the context of a broader thesis on CASE for natural products, this analysis provides detailed application notes and protocols for leading commercial and open-source platforms. The evolution of CASE, from early fragment-based systems to modern expert systems capable of handling complex natural products with 40 or more heavy atoms, underscores its growing indispensability in the researcher's toolkit [80] [3].

The landscape of CASE software includes mature commercial suites and emerging open-source alternatives, each with distinct approaches to the elucidation workflow. The following table summarizes the core characteristics, strengths, and primary use cases for four key platforms.

Table: Comparative Analysis of Leading CASE Platforms

Platform	Provider / Type	Core Methodology	Key Strength	Typical Use Case
ACD/Structure Elucidator	ACD/Labs (Commercial)	Expert system with automated Molecular Connectivity Diagram (MCD) generation and structure ranking [34].	Most peer-reviewed; high performance for complex, novel natural products; integrated fragment and database search [34] [3].	De novo elucidation of complex, unprecedented structures in natural product research.
Mnova Structure Elucidation	Mestrelab (Commercial)	Integrated workflow within Mnova NMR suite, utilizing the COCON structure generator [80] [35].	User-friendly interface; seamless with NMR processing; robust for routine small molecule elucidation [35].	Streamlined structure determination in organic chemistry and drug discovery workflows.
Bruker CMC-se	Bruker (Commercial)	Automated analysis integrated with AVANCE NMR spectrometers; focuses on correlation table generation [42].	Tight instrument integration; robust handling of data imperfections; strong verification tools [42].	Structure verification and elucidation in labs using Bruker NMR systems, including academic teaching.
Sherlock	Open-Source	Modular system combining NMRium (peak picking), pyLSD (structure generation), and ranking algorithms [80].	Full user control and transparency; customizable workflow; facilitates methodology research and education [80].	Academic research, method development, and educational purposes where cost and openness are priorities.

Detailed Application Notes and Protocols

3.1 Generic CASE Workflow for Natural Products A standard CASE protocol begins with the acquisition of a purified compound. The minimum recommended dataset includes 1D ¹H and ¹³C NMR, and 2D spectra (HSQC, HMBC, COSY) to define through-bond connectivities [34]. The molecular formula, typically determined by high-resolution mass spectrometry (HR-MS), is a critical input. The subsequent computational workflow is illustrated below.

Diagram Title: Standard Computer-Assisted Structure Elucidation (CASE) Workflow

3.2 Platform-Specific Protocols

• Protocol for ACD/Structure Elucidator:
  • Data Import & Processing: Import raw or processed NMR data and the molecular formula. Use the software's tools for peak picking and multiplet analysis [34].
  • MCD Generation & Editing: The software automatically generates a Molecular Connectivity Diagram (MCD). Manually review and edit atom properties (hybridization) or add/remove correlations based on chemical knowledge [34].
  • Structure Generation: Run the structure generator. For complex molecules, use "fragment mode" to search a library of 2.2+ million fragments for plausible starting points [34].
  • Ranking with DP4 Analysis: Review candidates ranked by average chemical shift deviation. Apply the integrated DP4 probability metric to statistically evaluate the best fit [34].
  • Stereochemistry Determination: Input NOESY/ROESY data to generate and rank 3D configurational models from the selected 2D structure [34].

• Protocol for Sherlock (Open-Source):
  • Spectral Peak Picking: Use the integrated NMRium module to manually or automatically pick peaks from uploaded NMR spectra (1D and 2D) [80].
  • Define Constraints: Create a molecular formula constraint. Review the automatically extracted COSY/HMBC correlations and manually add forbidden/obligatory bonds if known fragments are present [80].
  • Structure Generation via pyLSD: Execute the structure generation engine. For challenging cases, adjust generation parameters (e.g., use "first fragment" mode) to reduce the combinatorial space and computation time [80].
  • Review and Select: Examine the list of generated structures and their average deviation. The correct structure was ranked #1 in 37 out of 45 test cases using default settings [80].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table: Key Research Reagent Solutions for CASE-Supported Natural Product Elucidation

Item	Function & Role in CASE Workflow
Deuterated NMR Solvents (e.g., CDCl₃, DMSO-d₆, CD₃OD)	Provides a stable, non-interfering magnetic environment for acquiring high-resolution, reproducible NMR spectra, which is the foundational data for all CASE systems.
MS-Grade Solvents & Ionization Agents (e.g., Formic acid, TFA, Na/Ag salts for cationization)	Essential for obtaining high-resolution mass spectrometry (HR-MS) data to determine the exact molecular formula, a critical constraint for structure generation [34].
NMR Reference Standards (e.g., Tetramethylsilane (TMS), solvent residual peaks)	Provides a universal chemical shift (δ) reference for accurate peak picking and calibration, ensuring data consistency across platforms and laboratories.
Chromatography Media (e.g., Sephadex LH-20, C18 silica)	For the purification of natural product extracts to obtain the single compound required for classical CASE analysis, moving beyond direct mixture analysis [39].
Structure Verification Compounds (e.g., Mosher's esters, chiral shift reagents)	Used to experimentally determine absolute configuration, providing critical validation for stereochemical assignments proposed by CASE systems using NOE/ROE data.

Analysis of Technical Limitations and Future Directions

Despite their power, current CASE systems face inherent limitations. A primary challenge is their dependence on correct and unambiguous peak picking from 2D NMR spectra; errors here directly propagate to invalid structural constraints [34]. Furthermore, CASE algorithms can struggle with molecules exhibiting high symmetry or regions of low proton density, where insufficient HMBC correlations create ambiguous connectivity networks [81]. The elucidation of stereochemistry remains less automated than planar structure determination, often requiring additional experimental data.

Future development is focused on integrating artificial intelligence and machine learning to address these gaps. Emerging tools like DeepSAT use convolutional neural networks (CNNs) to directly predict structural features or identify known analogues from HSQC spectra alone, potentially streamlining the initial dereplication and fragment identification steps [82]. Another trend is the move towards the analysis of complex mixtures without full purification, using techniques like DOSY, STOCSY, and machine learning models to deconvolute spectra and identify bioactive components [39] [83]. The continued expansion of open-source platforms and shared data formats (e.g., NMReData) promises to enhance reproducibility, collaboration, and the development of next-generation, hybrid AI-CASE systems [80] [42].

The Role of Machine Learning and AI in Enhancing Prediction Accuracy and Speed

The discovery and development of therapeutics from Natural Products (NPs) represent a historically rich yet notoriously challenging frontier in drug discovery [84]. NPs possess unique chemical diversity and potent bioactivity, but their structural complexity, heterogeneity, and the labor-intensive process of isolation and characterization create significant bottlenecks [85] [84]. Computer-Assisted Structure Elucidation (CASE) has emerged as a critical discipline to address these challenges, systematically applying computational methods to determine the chemical structures of unknown compounds. Traditionally, CASE has relied on spectroscopic data interpretation and empirical rules. Today, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming CASE workflows, dramatically enhancing both the accuracy and speed of predictions [85] [86].

This transformation is driven by AI's capacity to discern complex, non-linear patterns within multidimensional data—such as NMR spectra, mass spectrometry fragmentation patterns, and genomic sequences—that elude conventional analysis [84] [87]. Within the context of a broader thesis on CASE for natural products research, this article details the application of AI/ML protocols, focusing on their role in accelerating the journey from raw spectroscopic data to a confidently elucidated, biologically relevant structure. The fusion of AI with advanced computational chemistry and experimental validation is paving the way for a new era of efficient, data-driven natural product discovery [88] [86].

Core AI/ML Methodologies in Modern CASE

The enhancement of CASE by AI is underpinned by several advanced computational methodologies. These approaches move beyond simple pattern matching to enable predictive modeling, generative design, and integrative analysis of multimodal data.

Graph Neural Networks (GNNs) and Molecular Representation: GNNs have become a cornerstone for representing chemical structures within ML models. In a CASE context, a molecule is treated as a graph where atoms are nodes and bonds are edges. GNNs can learn features directly from this graph structure or from spectroscopic data mapped onto it. This is particularly powerful for interpreting 2D-NMR datasets (like HSQC, HMBC), where proton-carbon correlation data can be formulated as connectivity graphs. Models such as Algebraic Graph Learning (AGL) scoring functions use weighted colored subgraphs from 3D protein-ligand complexes to predict binding affinities, a principle adaptable to scoring plausible structural interpretations against spectral data [89].

Generative AI for Structure Proposal and Expansion: Generative models, including Generative Adversarial Networks (GANs) and Transformer-based architectures, are used to propose novel chemical structures that conform to both spectral constraints and desirable chemical properties. For instance, the NPDL-GEN model combines a GPT-like generator with an Augmented Hill-Climb strategy to produce synthetically accessible "pseudo-natural products" with optimized drug-likeness [90]. In a CASE pipeline, such models can generate a diverse set of candidate structures that explain observed spectroscopic features, expanding the solution space beyond known databases.

Knowledge Graphs for Multimodal Data Integration: A significant challenge in NP research is the fragmentation of data across modalities (genomics, metabolomics, spectroscopy, bioactivity) [87]. Knowledge Graphs (KGs) provide a powerful framework to unify these disparate data types. Entities (e.g., a compound, a biosynthetic gene cluster, a spectral peak, a biological target) are represented as nodes, and their relationships (e.g., "produces," "correlates with," "inhibits") as edges. AI models can then traverse this graph for reasoning and prediction. For example, a KG can link a mass spectrometry feature to a predicted biosynthetic pathway from a genome, suggesting a potential structural class before isolation is complete. Frameworks like the Experimental Natural Products Knowledge Graph (ENPKG) demonstrate how this integration enriches data interpretation and discovery [87].

Hybrid Physics-Based and ML Models: Pure data-driven ML models can sometimes lack physical realism or perform poorly outside their training domain. Hybrid models that integrate physics-based simulations with ML are gaining traction. In kinetics and free energy prediction, hybrid Quantum Mechanical/Machine Learning (QM/ML) models achieve high accuracy at a fraction of the computational cost of pure ab initio methods [91]. For CASE, this approach can be used to predict NMR chemical shifts or coupling constants with quantum-mechanical accuracy but at high speed, providing a critical benchmark for proposed structures.

Table 1: Core AI/ML Methodologies and Their Applications in CASE Workflows

Methodology	Key Features	Primary Application in CASE	Impact on Speed/Accuracy
Graph Neural Networks (GNNs)	Learns directly from molecular graph structure; handles relational data.	Interpreting 2D-NMR correlation data; scoring structure-spectra matches [89].	High Accuracy: Improves interpretation of complex spin systems. Speed: Automates connectivity mapping.
Generative Models (GANs, Transformers)	Generates novel molecular structures from learned distributions.	Proposing candidate structures consistent with spectral data; designing pseudo-natural products [90].	High Speed: Rapidly enumerates plausible candidates. Expanded Scope: Explores novel chemical space.
Knowledge Graphs (KGs)	Integrates heterogeneous, multimodal data into a relational network.	Unifying genomic, spectroscopic, and bioactivity data for holistic candidate prioritization [87].	High Accuracy: Contextualizes evidence from multiple sources. Speed: Facilitates rapid data retrieval and hypothesis generation.
Hybrid QM/ML Models	Combines physics-based simulation with data-driven ML efficiency.	Predicting accurate NMR parameters (δ, J) and stability/conformation of candidates [91].	High Accuracy: Near-QM accuracy for properties. Speed: Orders of magnitude faster than full QM calculation.

Experimental Protocols & Application Notes

Protocol 1: AI-Enhanced Spectroscopic Data Interpretation and Structure Assembly

Objective: To automatically interpret 1D/2D-NMR and MS data and assemble a ranked list of plausible molecular structures. Background: Traditional structure elucidation relies on expert manual interpretation of spectra. This protocol uses a GNN-based pipeline to automate the process, significantly reducing time and subjectivity [89].

Materials & Input Data:

• Raw Spectroscopic Data: ¹H NMR, ¹³C NMR, HSQC, HMBC, COSY, and HR-MS data of the purified unknown NP.
• Software: GNN-based scoring software (e.g., adapted from AGL-EAT-Score principles [89]); cheminformatics toolkit (e.g., RDKit); spectral database for dereplication.
• Computational Resources: Workstation with GPU (e.g., NVIDIA V100 or equivalent) for model inference.

Procedure:

• Data Preprocessing: Convert raw spectra to standardized data tables. For NMR, extract chemical shift lists, integration values, and J-coupling constants. For MS, extract exact mass and key fragment ions.
• Molecular Formula Determination: Use HR-MS data to calculate possible molecular formulas within a defined error margin (e.g., < 3 ppm). Apply heuristic rules (e.g., Nitrogen Rule) to filter possibilities.
• Fragment Identification & Graph Construction: The AI model analyzes HSQC/COSY data to identify proton-carbon pairs and proton-proton spin systems. Each heavy atom (C, O, N) is initialized as a graph node. HMBC long-range correlations are used to propose edges (bonds) connecting these fragments, building a set of "partial structure graphs."
• Candidate Generation & Ranking: A generative model or a structure assembly algorithm enumerates complete molecular structures consistent with the partial graphs and molecular formula. Each candidate is scored by a GNN model trained to predict the likelihood of its spectra matching the input experimental data.
• Dereplication & Output: The top-ranked structures are automatically queried against natural product databases (e.g., COCONUT, NP Atlas) for dereplication. The final output is a report listing the top 5-10 candidate structures with associated confidence scores and supporting spectral correlations.

Protocol 2: Generative Design of Pseudo-Natural Product Libraries

Objective: To generate novel, synthetically accessible compound libraries inspired by natural product scaffolds for virtual screening. Background: This protocol leverages the NPDL-GEN framework to overcome the limitations of natural product availability and complexity by generating optimized "pseudo-NPs" [90].

Materials & Input Data:

• Training Data: A cleaned and standardized dataset of known natural product structures (e.g., from PubChem or in-house libraries) in SMILES format.
• Software: Implementation of the NPDL-GEN model (GPT generator + Augmented Hill-Climb optimizer) [90]; synthetic accessibility assessment tool (e.g., SAscore).
• Computational Resources: High-performance computing cluster for model training and large-scale generation.

Procedure:

• Model Training: Train the GPT-like generator on the curated NP structure dataset. The model learns the statistical distribution of chemical motifs, rings, and functional groups characteristic of NPs.
• Seeded Generation: Initiate generation using seed fragments derived from a NP of interest or from a random sampling of the latent space of the trained model.
• Optimization via Augmented Hill-Climb (AHC): For each generated molecule, the AHC algorithm iteratively makes small structural modifications. After each modification, the molecule is evaluated against a multi-parameter fitness function that typically includes:
  • Drug-likeness: Quantitative Estimate of Drug-likeness (QED).
  • Synthetic Accessibility (SA): Penalizes overly complex or inaccessible structures.
  • Structural Novelty: Distance from nearest neighbor in the training set.
• Library Curation & Validation: The optimized molecules are filtered based on desired physicochemical property ranges (MW, logP, etc.). A subset is subjected to in silico ADMET prediction and, if possible, synthesis planning via a retrosynthesis AI tool [91].
• Integration with CASE: The final library can be used as a custom database for in-house virtual screening campaigns or serve as a source of candidate structures when experimental CASE for an unknown compound yields ambiguous results.

Protocol 3: Building a Knowledge Graph for Context-Aware Structure Elucidation

Objective: To integrate disparate data sources into a unified Knowledge Graph to provide biological and chemical context for a structure elucidation project. Background: This protocol outlines steps to construct a domain-specific KG, enabling AI to reason across genomics, metabolomics, and literature, thereby informing and validating structural hypotheses [87].

Materials & Input Data:

• Data Sources: In-house LC-MS/MS metabolomics data; (meta)genomic assemblies and predicted Biosynthetic Gene Clusters (BGCs); public NP databases; bioassay results; relevant scientific literature.
• Tools: Graph database (e.g., Neo4j, Amazon Neptune); NLP/text-mining tools for literature processing; ontology frameworks (e.g., ChEBI, NCBITaxon).

Procedure:

• Schema & Ontology Design: Define the node and edge types for the KG. Core nodes include Compound, Spectrum, BGC, Organism, BiologicalTarget, Assay. Key relationships include Organism_PRODUCES_Compound, Compound_HAS_MS2_Spectrum, BGC_ENCODES_BiosynthesisOf, Compound_INHIBITS_Target.
• Data Ingestion & Entity Linking: Populate the graph by importing structured data (e.g., compound databases) and extracting information from unstructured text (literature, lab notes) using NLP. A critical step is entity linking, where, for example, a mention of "artemisinin" in a paper is linked to the canonical Compound node for artemisinin in the database.
• Hypothesis Generation for an Unknown: When an unknown compound (Unknown_1) is under investigation, its observed properties (molecular ion, fragment ions, source organism) are added as nodes. The KG is then queried via graph algorithms:
  • Find organisms phylogenetically close to the source organism and list the known compounds they produce.
  • Find BGCs in the source organism's genome that are similar to BGCs known to produce compounds with similar MS/MS patterns.
• Contextual Validation: A proposed structure for Unknown_1 can be validated by checking the KG for existing compounds with similar structures and their reported biological activities. If the source organism's extract shows a specific bioactivity, and the proposed structure is linked (via the KG) to a known target relevant to that activity, confidence in the elucidation increases.
• Continuous Learning: The KG is a living resource. Newly elucidated structures, their spectral data, and bioactivity results are fed back into the graph, enriching it for future queries and enabling the discovery of broader patterns across the NP space.

Visualization of AI-Enhanced CASE Workflows

AI-Driven CASE Workflow Integrating ML & Knowledge

Knowledge Graph Reasoning for Structure Elucidation

The Scientist's Toolkit: Essential Research Reagents & Solutions

Table 2: Key Research Reagent Solutions for AI-Enhanced CASE Protocols

Category	Item / Solution	Function in CASE Workflow	Example / Note
Computational Software	GNN-based Scoring & Docking Software	Scores protein-ligand binding affinity or structure-spectra match; critical for ranking candidates [88] [89].	Gnina (CNN-based scoring) [89], AGL-EAT-Score [89].
	Generative Chemistry Platforms	Generates novel, valid molecular structures conditioned on desired properties or spectral constraints [90].	NPDL-GEN model [90], Molecular Transformer models.
	Retrosynthesis & Reaction Prediction AI	Predicts feasible synthetic routes for proposed or generated structures, assessing synthetic accessibility [91].	Tools leveraging Monte Carlo Tree Search (MCTS) & neural networks [91].
	Knowledge Graph Database System	Stores and queries interconnected multimodal data for context-aware reasoning [87].	Neo4j, Amazon Neptune, or custom solutions using RDF frameworks.
Data & Libraries	Curated Natural Product Databases	Provides ground-truth data for training AI models and for dereplication [84] [87].	COCONUT, NP Atlas, LOTUS, in-house spectral libraries.
	Ultra-Large Virtual Compound Libraries	Provides billions of synthesizable structures for virtual screening alongside CASE candidates [88].	Enamine REAL Space, ZINC, SAVI [88].
	Pre-trained AI Models	Off-the-shelf models for property prediction (ADMET, pKa, logP) and spectral simulation [91] [89].	Attentive FP models (e.g., AttenhERG for toxicity) [89], QM/ML models for property prediction [91].
Instrumentation & Analysis	High-Field NMR with Cryoprobes	Generates high-resolution, sensitive 1D and 2D NMR data, the primary input for structure-focused AI models.	600 MHz+ spectrometers; essential for complex NPs [92].
	High-Resolution LC-MS/MS Systems	Provides exact mass and fragmentation data for molecular formula determination and AI-based spectral matching.	Q-TOF or Orbitrap systems coupled with UHPLC.
	Automated Fractionation & Microtiter Platforms	Generates the purified samples and bioactivity data that feed into the CASE and KG workflows.	Enables high-throughput generation of training and validation data.

Quantitative Performance & Impact Assessment

The integration of AI into CASE is not merely theoretical but is delivering measurable improvements in key performance metrics. These advancements directly address the core challenges of time, cost, and success rates in natural product-based drug discovery.

Speed Enhancements: AI dramatically accelerates the most time-consuming steps. For instance, retrosynthesis planning using neural-symbolic frameworks and Monte Carlo Tree Search can generate expert-quality synthetic routes in minutes, a task that previously took human experts days [91]. In virtual screening, GPU-accelerated docking and ML-based scoring functions enable the screening of ultra-large libraries (billions of compounds) in practical timeframes, a process that was computationally prohibitive a few years ago [88] [86]. Spectral analysis is also expedited; ML models can predict NMR chemical shifts or assign spectra in seconds, compared to hours of manual analysis.

Accuracy Improvements: The predictive accuracy of AI models directly translates to more reliable structure elucidation. Hybrid QM/ML models achieve near-quantum mechanical accuracy for predicting properties like free energy of binding or pKa while being several orders of magnitude faster [91]. For binding affinity prediction, modern GNN and transformer-based models consistently outperform classical scoring functions, reducing false positives in virtual screening [89]. In generative design, models like NPDL-GEN produce molecules with high validity (>95%), uniqueness, and improved drug-likeness profiles, ensuring that proposed structures are both chemically realistic and therapeutically relevant [90].

Table 3: Quantitative Impact of AI/ML on Key Drug Discovery Parameters

Performance Metric	Traditional / Baseline Approach	AI-Enhanced Approach	Documented Improvement / Impact	Source
Virtual Screening Hit Rate	Typically 1-10% from HTS or standard VS.	Structure-based VS with ML scoring on ultra-large libraries.	Hit rates of 10-40% reported, with identification of nM-potency leads [88].	[88]
Retrosynthesis Route Planning Speed	Human expert requires hours to days per molecule.	Neural-symbolic AI with MCTS.	Generates expert-quality routes in minutes to seconds [91].	[91]
Free Energy Calculation Speed	Ab initio QM methods: high accuracy but extremely slow.	Hybrid QM/ML models.	Achieves high accuracy at computational cost reduced by several orders of magnitude [91].	[91]
Property Prediction (ADMET) Accuracy	Traditional QSAR models with limited descriptors.	Deep learning models (e.g., Graph Neural Networks).	Consistently achieve state-of-the-art accuracy in benchmarks (e.g., Attentive FP for hERG) [89].	[89]
Generative Model Output Quality	Early generators produced many invalid/unnatural structures.	Advanced architectures (GPT + AHC optimization).	Generates molecules with >95% validity, high novelty, and optimized drug-likeness [90].	[90]
Overall R&D Cost & Timeline	Average cost ~$2.6B and 10-15 years per drug.	CADD/AI integration across pipeline.	Estimated to reduce discovery costs by up to 50% and significantly shorten timelines [88] [86].	[88] [86]

The integration of AI and ML into Computer-Assisted Structure Elucidation marks a paradigm shift for natural products research. As detailed in these application notes and protocols, AI is not a standalone tool but a synergistic force that enhances every stage of the workflow—from rapid, accurate interpretation of complex spectroscopic data to the generative design of novel analogs and the integrative reasoning provided by knowledge graphs. This directly contributes to the core thesis that modern CASE must evolve into an intelligent, predictive, and context-aware framework to fully unlock the potential of natural products.

Future advancements within this field will likely focus on several key areas. First, the development of foundational models for chemistry, pre-trained on massive, multimodal datasets, which can be fine-tuned for specific CASE tasks with limited data, addressing the challenge of small, imbalanced NP datasets [85]. Second, the tighter integration of robotic and automated laboratory systems with AI platforms will close the loop between in silico prediction and in vitro validation, creating self-driving "laboratories" for NP discovery [85] [86]. Finally, as AI models become more central, ensuring their interpretability and reliability through uncertainty quantification and applicability domain estimation will be critical for gaining the trust of researchers and meeting regulatory standards [85]. By embracing these directions, the next generation of CASE systems will not only elucidate structures faster but will also intelligently predict their function, synthesis, and therapeutic value, radically accelerating the translation of natural product complexity into clinical opportunity.

Determining the correct three-dimensional structure of a natural product is a foundational step in drug discovery, as biological activity is intimately governed by molecular architecture [93]. An erroneous structural assignment can derail a research program, wasting years of effort and resources. High-profile cases, such as the anticancer candidate TIC10, underscore the severe consequences—including legal disputes over intellectual property—when a published structure is later revised [93]. While techniques like NMR spectroscopy and X-ray crystallography are indispensable, the interpretation of complex spectroscopic data for novel, intricate natural products remains a significant inverse problem, inherently susceptible to investigator bias [93] [4]. Computer-Assisted Structure Elucidation (CASE) systems have emerged as a transformative preventive tool. By applying logical-combinatorial algorithms and leveraging comprehensive databases, CASE provides an objective, systematic framework for structure generation and ranking. This methodology minimizes human error at the outset, thereby preventing the costly and reputation-damaging revisions that occur when misassignments are discovered years later through total synthesis or computational re-analysis [93] [4].

Core CASE Protocols for Error-Resistant Structure Elucidation

The following protocols outline a standardized, preventive workflow for de novo structure elucidation of natural products using a modern CASE system, such as the ACD/Structure Elucidator Suite [34].

Protocol: Data Acquisition and Curation for CASE Analysis

The reliability of a CASE solution is directly contingent on the quality and completeness of the input spectroscopic data.

• Step 1: Determine Molecular Formula: Obtain a high-resolution mass spectrum (HRMS) to establish the exact molecular formula. This is a critical constraint that dramatically reduces the combinatorial space for structure generation [34].
• Step 2: Acquire a Minimum NMR Dataset: Collect a core set of 2D NMR spectra on a purified sample. A CASE system requires a minimum set of 1D ¹H, COSY, ¹H–¹³C HSQC, and ¹H–¹³C HMBC spectra [34]. The HMBC experiment, optimized for long-range couplings (typically ~8 Hz), is particularly crucial for defining carbon-carbon connectivity.
• Step 3: Peak Picking and List Preparation: Meticulously pick peaks from all 2D spectra. Action: Export chemical shift lists and correlation data in a software-agnostic format (e.g., peak lists with δH, δC, and correlation type). Consistency in referencing and accurate distinction between direct (HSQC) and long-range (HMBC) correlations is essential.
• Step 4: Data Consistency Check: Prior to importing into the CASE software, logically review data for common errors. Check for: Missing HSQC correlations for protonated carbons, HMBC correlations from non-protonated carbons, and symmetry. This manual pre-check prevents the generation of nonsensical structures due to simple oversights.

Protocol: Molecular Connectivity Diagram (MCD) Generation and Editing

The MCD is a 2D map of atom connectivity inferred from the spectral data and is the cornerstone of the CASE process [34].

• Step 1: Automated MCD Creation: Import the molecular formula and curated peak lists into the CASE software. The system will automatically generate an initial MCD. Atoms are represented as nodes (colored by hybridization state from predicted chemical shifts), and spectroscopic correlations are represented as bonds of different types (e.g., definite, possible) [34].
• Step 2: MCD Editing and Knowledge Integration: Critically review the automated MCD. Action: Use chemical knowledge to edit the diagram:
  • Define Obligatory Bonds: If a specific functional group or fragment is known (e.g., from IR or biosynthetic reasoning), lock in these connectivities.
  • Define Forbidden Bonds: Prohibit chemically impossible connectivities (e.g., bonds that would create an impossible ring size or violate valence rules).
  • Label "Nonstandard" Correlations: Identify and flag HMBC correlations that exceed the typical 2-3 bond range. The CASE system can then perform "fuzzy structure generation" to account for these, which is vital for solving complex problems [4].

Protocol: Structure Generation, Ranking, and Validation

This protocol transforms the MCD into a ranked list of candidate structures.

• Step 1: Structure Generation: Initiate the structure generator. Using the constraints of the molecular formula and the edited MCD, the software will assemble all constitutional isomers that satisfy the provided spectroscopic correlations [34] [4]. For complex molecules, this may generate thousands of candidates.
• Step 2: Chemical Shift Prediction and Ranking: The software calculates ¹³C (and ¹H) NMR chemical shifts for each candidate structure. Action: Employ a high-fidelity prediction algorithm, such as a neural network or increment-based method. Candidates are ranked by the average deviation (dₙ) between their predicted shifts and the experimental data [34]. The structure with the lowest dₙ is typically the most probable.
• Step 3: Probabilistic Validation (DP4 Analysis): For the top-ranked candidates, apply a DP4 probability analysis. This statistical method calculates the probability that each candidate is correct based on the combined ¹³C and ¹H chemical shift distributions, providing an objective metric to resolve ambiguity [34].
• Step 4: Final Review and Reporting: Cross-verify the top-ranked structure. Action: Manually confirm key HMBC correlations map correctly. Use the software to create a publication-ready report documenting the input data, MCD, generated structures, ranking statistics, and the final assignment.

Table 1: Key Features of a Modern CASE Software Platform (e.g., ACD/Structure Elucidator Suite) [34]

Feature Category	Specific Function	Role in Error Prevention
Data Integration	Vendor-neutral import of NMR, MS, IR data	Prevents manual transcription errors, ensures data consistency
Connectivity Mapping	Automated Molecular Connectivity Diagram (MCD) generation	Provides an objective, visual map of data constraints before interpretation bias occurs
Knowledge Integration	Editable MCD with user-defined fragment libraries (e.g., 2.2+ million fragments)	Allows incorporation of prior chemical knowledge (e.g., biosynthetic precursors) to guide the solver
Structure Generation	Deterministic structure generator creating all isomers fitting constraints	Ensures the correct structure is within the evaluated set, preventing oversight
Prediction & Ranking	Neural network/HOSE-based chemical shift prediction; ranking by average deviation (dₙ)	Provides an unbiased, quantitative comparison of candidate fits to experimental data
Statistical Validation	DP4 probability analysis	Offers a robust statistical measure to confirm the top candidate, reducing reliance on intuition
Dereplication	Search against internal & external databases (e.g., PubChem)	Immediately identifies known compounds, preventing redundant "rediscovery" and misassignment

Application Note: CASE in Post-Elucidation Structural Validation and Revision

CASE is not only a tool for solving unknown structures but also a powerful independent auditor for validating published assignments and diagnosing errors in proposed structures.

Scenario: A newly isolated compound or a literature structure exhibits biological activity, but the proposed structure feels anomalous or synthetic efforts fail.

Validation Protocol:

• Data Compilation: Gather the published spectroscopic data (NMR chemical shifts, correlations, molecular formula).
• CASE Analysis of the Proposed Structure: Input the data as per Section 2 protocols. Action: Start by forcing the MCD to conform to the published structure. Run chemical shift prediction and note the dₙ value. A high dₙ (> 3-5 ppm for ¹³C) indicates a poor fit and potential misassignment [93].
• De Novo Elucidation: Remove structural constraints and run a standard CASE workflow on the published data. Let the software generate all possible isomers from the data.
• Comparative Analysis: Compare the ranking of the originally proposed structure against the new candidate set. If the published structure is not the top candidate, the CASE output provides chemically plausible alternatives that better fit the experimental evidence.
• Diagnosis & Revision: Analyze the discrepancies. Common errors revealed by CASE include incorrect halogen placement (as in tristichone C), mistaken stereochemistry, or even an entirely incorrect carbon skeleton [93]. The software provides quantitative metrics (dₙ, DP4) to support the revision.

Case Study – Aldingenin C [93]:

• Original Assignment: Structure 3a.
• Discrepancy: Synthetic material matching 3a did not match natural product spectra.
• CASE-Driven Revision: CASE analysis of the original NMR data generated a candidate list where the known compound caespitol (3b) was the top-ranked structure, demonstrating a perfect spectral match. This led to the correction of not just one, but a series of related compounds.

Table 2: Documented Structural Revisions Resolved via Computational and CASE Methods [93]

Natural Product	Original Structure Issue	Method Used for Revision	Key to Correction
Aldingenins A-D	Incorrect carbon skeleton & halogen placement	CASE analysis & synthesis	CASE identified structures as known caespitols; rFF computational NMR confirmed.
Decurrensides A-E	Incorrect hemiacetal/anhydro core configuration	DFT-GIAO δc & rFF J-coupling calculations	Computed ¹³C shifts and coupling constants of proposed structure deviated severely from experiment.
Tristichone C	Misassigned positions of Br and Cl atoms	rFF with quadratic scaling for halogen shifts	Predicted δC for Cl-bearing carbon in original structure deviated by 6.6 ppm; isomer fit perfectly.
Glabramycins B & C	Likely incorrect stereochemistry or substitution	Not specified in excerpt; commonly DP4 analysis	CASE and DP4 analysis of stereoisomers can identify the best-fit configuration.

Table 3: Key Research Reagent Solutions and Materials for CASE Experiments

Item	Function in CASE Workflow	Critical Specifications & Notes
High-Field NMR Spectrometer	Acquires the essential 2D NMR data (HSQC, HMBC, COSY).	Field Strength: ≥ 500 MHz for ¹H frequency. Probe: Cryogenically cooled inverse detection probe for sensitivity; preferably equipped for ¹H-¹³C and ¹H-¹⁵N.
High-Resolution Mass Spectrometer	Determines the exact molecular formula, a primary constraint for structure generation.	Resolution: > 30,000 (FWHM). Accuracy: < 3 ppm mass error. Modes: ESI⁺/⁻, APCI, etc., as suitable for the analyte.
CASE Software Suite	The core platform for data integration, MCD generation, structure generation, and prediction.	Essential Features: Vendor-neutral data import, editable MCD, deterministic structure generator, high-accuracy NMR predictor (neural net/HOSE), statistical validation tools (DP4), and dereplication links [34].
Fragment & Chemical Databases	Enables dereplication and provides prior knowledge for MCD editing.	Examples: Internal CASE fragment library (2M+ fragments), PubChem, CAS SciFinderⁿ, Dictionary of Natural Products. Integrated searching is key [34].
Deuterated NMR Solvents	Provides the lock signal and solvent environment for NMR experiments.	Grade: 99.8+% D. Common solvents: CDCl₃, DMSO-d₆, CD₃OD, D₂O. Must be dry and appropriate for the sample.
Reference Compounds	Used for spectral calibration and as internal standards for chemical shift referencing.	Example: Tetramethylsilane (TMS) at 0.0 ppm. Alternatively, residual solvent peaks can be used (e.g., CHCl₃ in CDCl₃ at 7.26 ppm for ¹H).

Within the broader thesis on Computer-Assisted Structure Elucidation (CASE) for natural products research, the imperative to objectively evaluate the performance of these expert systems is paramount [22] [1]. The structure elucidation of complex natural products remains a significant intellectual challenge, with a persistent risk of incorrect assignments appearing in the literature [1] [11]. CASE systems, which synergistically combine spectroscopic data (primarily NMR and MS) with computational chemistry and algorithmic structure generation, promise to mitigate this risk by ensuring all plausible structural candidates consistent with the data are considered [22] [53]. This document provides detailed application notes and protocols for benchmarking the performance of CASE platforms. It focuses on assessing their efficacy in solving structures of known compounds (thereby validating the method) and explicitly underscores the inherent limitations encountered with specific structural classes or data quality issues [94] [53]. The goal is to furnish researchers and drug development professionals with a standardized framework for evaluating and implementing CASE in their natural product discovery workflows [11].

Quantitative Benchmarking of CASE Systems: Performance Metrics

The most rigorous assessment of CASE software comes from blind trials, where the solution is unknown to the analyst. A long-term, global study involving 112 challenges provides key quantitative metrics for benchmarking [53].

Table 1: Summary of Blind Trial Performance for a CASE System (ACD/Structure Elucidator)

Performance Metric	Result	Details & Context
Total Challenges Analyzed	112	Collected over ~9 years from academic (50%), pharmaceutical (42%), industrial (5%), and government (3%) sectors [53].
Double-Blind Trials	10 (9%)	Structure unknown to both submitter and analyst; high scientific value [53].
Success Rate (Agreement)	100 challenges (89.3%)	"Double Agreement" (structure validated) and "Single Agreement" (top candidate not formally confirmed) [53].
Failure Rate (Incorrect/Rejected)	12 challenges (10.7%)	Incorrect solutions or data rejected due to poor quality or insufficiency [53].
Average Total Processing Time	~84 minutes (~1.4 hours)	Includes spectral data processing, dereplication, and structure generation [53].
Average Structure Generation Time	>25 minutes	Generates an average of ~2,639 candidate structures per challenge [53].
Dereplication Library Search Time	~3 minutes	Searched against a library of >19 million records with 13C chemical shift data [53].

Performance is heavily influenced by the nature of the molecular problem. The table below categorizes challenges that increase complexity and risk of failure.

Table 2: Problem Complexity and Associated CASE Limitations

Challenge Category	Impact on CASE Performance	Potential Outcome
Deficiency in Protons	Limits available 1H-13C correlation data (HSQC), reducing constraints.	Increased number of candidate structures; possible failure [1] [53].
High Molecular Symmetry	Reduces the number of independent structural constraints from NMR data.	Ambiguity in structure generation; can produce incorrect, overly symmetric solutions [53].
Large Number of Heteroatoms (N, O, S, P)	Expands the combinatorial possibilities for atom connectivity.	Explosive growth in candidate structures; requires very high-quality HMBC data [53].
Poor Quality Spectral Data	Low S/N, incorrect peak picking, impurities lead to false or missing constraints.	Generation of incorrect structures or failure to generate any valid structure [53].
Very Large Molecules (>1000 Da)	Surpassed software algorithm limits in earlier versions.	Software failure; limitations typically addressed in subsequent updates [53].
Non-Standard Correlations (>3 bonds)	Violates the standard HMBC assumption, creating "contradictory" constraints.	May be flagged as "problematic" peaks; requires expert intervention to manage [1].

Experimental Protocols for CASE Benchmarking

Protocol 1: Sample Preparation & Data Acquisition for Natural Products

Objective: To generate comprehensive, high-quality spectroscopic data for an unknown natural product suitable for CASE analysis.

• Purification: Isolate the pure compound (>95% purity as assessed by analytical LC-MS) using hyphenated techniques (e.g., LC-MS-guided fractionation) [94] [11].
• Mass Spectrometry:
  • Acquire high-resolution mass spectrometry (HR-MS) data to determine the molecular formula (elemental composition) with high confidence (mass error < 3 ppm) [11].
  • Perform tandem MS/MS or multiple-stage MSn experiments to obtain fragmentation patterns [94].
• Nuclear Magnetic Resonance Spectroscopy:
  • Prepare a sample in an appropriate deuterated solvent.
  • Acquire 1D NMR: 1H and 13C NMR (with sufficient S/N for 13C).
  • Acquire Standard 2D NMR Suite:
    • HSQC: For direct 1H-13C correlations (one-bond connections).
    • HMBC: For long-range 1H-13C correlations (2-3 bonds, occasionally 4). Optimize for nJCH = 8 Hz.
    • COSY/TOCSY: For 1H-1H through-bond correlations (vicinal and geminal protons).
• Optional Advanced Data: For complex stereochemistry or validation, acquire:
  • NOESY/ROESY: For through-space proximities to aid in relative configuration determination [11].
  • Residual Dipolar Couplings (RDCs) or Chemical Shift Anisotropies (RCSAs): For absolute configuration and 3D structure refinement [22].

Protocol 2: CASE System Workflow & Structure Generation

Objective: To input spectroscopic data into a CASE system and generate all plausible structural candidates.

• Data Input and Preprocessing:
  • Import the molecular formula.
  • Input chemical shifts from 1H and 13C NMR spectra.
  • Input 2D correlation data: Define atom pairs from HSQC (1H-13C), HMBC (1H-13C), and COSY/TOCSY (1H-1H) peak lists. Manually verify and curate peak picking to remove noise and solvent artifacts [53].
• Molecular Connectivity Generation (Structure Generation):
  • The CASE algorithm uses the 2D NMR correlations as constraints to assemble molecular fragments and generate all possible planar structures (constitutional isomers) consistent with the data [1].
  • This process systematically explores the combinatorial possibilities defined by the molecular formula and correlation maps.
• Candidate Ranking and Selection:
  • Generated candidate structures are ranked based on the agreement between their predicted 13C NMR chemical shifts (using internal algorithms or databases) and the experimental chemical shifts [53].
  • The structure with the lowest average deviation (dN) is typically the top-ranked candidate.
• Output Analysis: The system provides a report listing ranked candidates, their predicted spectra, and the discrepancies from experimental data for expert review.

Protocol 3: Validation of the CASE Solution

Objective: To conclusively verify the top-ranked CASE candidate as the correct structure.

• Dereplication Check: Before accepting a novel structure, compare the top candidate's predicted and experimental data (MS, NMR) against commercial and public natural product databases (e.g., AntiBase, MarinLit, COCONUT, GNPS) [95] [11] to rule out a known compound.
• Density Functional Theory (DFT) NMR Calculation:
  • For the top 1-3 candidates, perform conformational search and geometry optimization using quantum chemical methods (e.g., DFT).
  • Calculate the theoretical NMR chemical shifts (13C and 1H) for the optimized conformer(s).
  • Statistically compare the calculated shifts with experimental data using methods like DP4 probability analysis [11]. A DP4 probability >95% provides strong evidence for the correct structure.
• Independent Data Correlation: If available, compare the CASE solution with biosynthetic gene cluster predictions from genomic data or with related compounds from the same source organism [11].

Diagram 1: CASE Workflow and Validation Protocol [1] [53] [11]

Diagram 2: Diagnostic Logic for Common CASE Limitations [1] [53]

Table 3: Key Reagents, Software, and Databases for CASE

Item / Resource	Function / Purpose	Notes & Examples
Deuterated NMR Solvents	Provide a signal-free lock and field-frequency stabilization for NMR experiments. Essential for 2D NMR.	DMSO-d6, CDCl3, CD3OD, D2O. Purity should be >99.9% D.
NMR Reference Standards	Provide internal chemical shift calibration for 1H and 13C spectra.	Tetramethylsilane (TMS, δ = 0 ppm) or residual solvent peaks.
CASE Expert Software	Core platform for algorithmic structure generation from spectroscopic constraints.	ACD/Structure Elucidator, SpecInfo, CMC-se [1] [53].
Quantum Chemistry Software	Perform DFT calculations to optimize geometry and predict NMR shifts for validation.	Gaussian, GAMESS, ORCA. Used for DP4 analysis [11].
Natural Product Databases	For dereplication: comparing data of unknown compounds against known compounds.	COCONUT (~400k NPs) [95], AntiBase, MarinLit, GNPS [11].
Spectral Libraries	For dereplication via spectral matching, especially MS and 13C NMR.	Integrated in CASE software (e.g., >19M 13C records) [53] or public (NIST MS, MassBank).
Cheminformatics Toolkits	For handling, standardizing, and analyzing chemical structures in silico.	RDKit, Chemistry Development Kit (CDK) [94] [95].

Discussion: Current Limitations and Future Perspectives

While CASE systems have matured into indispensable tools, critical limitations persist. Their primary output is the planar (2D) molecular structure [1]. Determining relative and absolute configuration (stereochemistry) often requires additional, specialized NMR experiments (e.g., NOESY, ROESY, J-based configurational analysis) or computational methods like DFT-based optical rotation calculation [22] [11]. Furthermore, CASE algorithms operate on defined assumptions, most notably that HMBC correlations represent 2- or 3-bond couplings. "Non-standard" correlations (e.g., over 4 bonds or through heteroatoms) can cause contradictions that require expert intervention to resolve [1].

The future "golden age" of CASE lies in deeper integration [22]. This includes synergy with:

• Advanced NMR Experiments: Techniques like pure-shift NMR, improved RDC/RCSA measurements, and ultrafast 2D NMR will provide richer, higher-quality input data [22].
• Machine Learning (ML): ML models are being trained to predict chemical shifts, suggest structural fragments, and prioritize candidate structures from vast virtual libraries, such as the 67 million natural product-like compounds generated via molecular language processing [95].
• Other Analytical Techniques: Correlating CASE with data from microcrystal electron diffraction (MicroED), atomic force microscopy (AFM), and comprehensive hyphenated chromatography (LC×LC, GC×GC) will provide orthogonal validation and solve problems intractable by NMR alone [22] [94].

For the natural products researcher, the pragmatic approach is to view the CASE system not as an autonomous oracle, but as a powerful co-pilot. The synergistic interaction between the chemist's chemical intuition and the computer's exhaustive, unbiased search capability dramatically improves the speed, throughput, and reliability of structure elucidation [53] [11]. Benchmarking, as outlined in these protocols, is the essential first step to adopting this partnership effectively.

Conclusion

Computer-Assisted Structure Elucidation (CASE) has matured into an indispensable, high-throughput tool that fundamentally accelerates and secures the discovery pipeline for natural products and drug candidates. By synthesizing the foundational principles, robust methodologies, targeted troubleshooting, and rigorous validation protocols explored in this article, researchers can confidently apply CASE to solve increasingly complex structural challenges. Future directions point toward deeper integration of artificial intelligence and machine learning for predictive accuracy, greater automation bridging data acquisition and interpretation, and expanded use in stereochemical determination and metabolomics. These advancements promise to further streamline biomedical research, reduce costly errors in structural assignments, and accelerate the translation of novel natural products into clinical therapies.

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