Scope of natural products in AI for drug discovery

IYH Dear Ifra Saifi Here is an analysis of the first review paper h/t Angelos Kollias

AI integrates -omics data (genomics, metabolomics) to predict novel natural products, their structures, and bioactivities, prioritizing candidates for lab testing.

Practical Deployment and Usability:

Real-World Use: AI can prioritize natural product candidates for lab testing, reducing time/cost in drug discovery. For example, CO-ADD’s standardized antibiotic screening protocol accelerates compound validation.
Non-Expert Accessibility: Tools like DECIMER (chemical image recognition) and ReDU (reanalysis of MS data) simplify data processing for researchers without AI expertise.
Example: Botanists studying medicinal plants could use AI to predict bioactive compounds from plant metabolomics data, guiding extraction and synthesis efforts.

Unexpected Findings:

Data Limitations: Despite advances in AI, high-quality datasets for natural products are scarce, hindering model training. This underscores the critical need for data standardization.

Overfitting Risks: AI models often fail to generalize due to biased or incomplete datasets, requiring rigorous validation (e.g., cross-validation, independent test sets).

BGC Prediction Challenges: Deep learning tools like DeepBGC improve BGC detection but still struggle with false positives/negatives, highlighting the need for hybrid approaches.

Structural Complexity: AI struggles to predict novel chemistry or enzyme activities not previously observed, limiting its ability to discover entirely new mechanisms.

Interoperability Gaps: Fragmented natural product databases lack standardized protocols, making data integration (e.g., linking metabolites to BGCs) difficult.

Approach:

Methodology: Genome Mining: Tools like DeepBGC and ClusterFinder predict BGCs from DNA sequences. Metabolome Analysis: AI algorithms (e.g., Spec2Vec) match mass spectrometry (MS) data to compound structures. Bioactivity Prediction: Models like ChemBERTa predict bioactivities from chemical structures, while tools like NPLinker integrate omics data.

Problem-Solving Techniques: Data Integration: Combining genomic, metabolomic, and phenotypic data to predict natural product targets. Algorithmic Advances: Using deep learning (e.g., graph neural networks) to model molecular interactions and predict novel compounds.

Results and Evaluation:

Key Findings: AI tools like DeepRiPP and DECIMER successfully identified novel natural products (e.g., deepflavo, rivulariapeptolides). Models integrating genomic and metabolomic data improved BGC-to-metabolite matching.

Quantitative Results: DeepBGC achieved 80% accuracy in BGC prediction (vs. 60% for rule-based methods). Spec2Vec improved MS similarity scoring by 30% compared to traditional methods.

Notable Achievements: AlphaFold’s protein structure prediction breakthroughs could aid in modeling natural product biosynthesis. NPLinker and GNPS enabled large-scale integration of omics data for compound discovery.

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