Chapter 4: Predicting Plant Secondary Metabolites
Synopsis
Author
Mrs. Shailaja Kali,
Assistant Professor, Department of Pharmacology, Sir C.R. Reddy College of Pharmaceutical Sciences, Eluru, Andhra Pradesh, India
Abstract
Treatment algorithms for cardiovascular disorders rely on evidence-based, stepwise clinical pathways. Decoding the complex chemical language of plants requires sound knowledge of genomics and metabolomic reality through the lens of computational biology. Artificial Intelligence enables the prediction of plant secondary metabolites directly from genomic sequences by identifying and annotating Biosynthetic Gene Clusters (BGCs), unlocking the "cryptic" metabolic pathways hidden within plant genomes that are silent under standard conditions. Machine learning models rigorously correlate genetic variants, such as Single Nucleotide Polymorphisms (SNPs) and differential gene expression profiles, with specific chemotypes, effectively mapping the complex genotype-phenotype relationship. Computational tools facilitate the structural elucidation of these complex molecules from spectroscopic data, including NMR and Mass Spectrometry, automating the interpretation of intricate spectral signatures that often challenge human analysis. Generative Adversarial Networks (GANs) extend this capability by proposing novel chemical structures based on learned biosynthetic rules, exploring the vast chemical space beyond known natural products to identify theoretical analogs with improved properties. These AI-driven approaches predict the bioactivity, solubility, and toxicity of these theoretical compounds, prioritizing high-value targets for physical isolation. This creates a virtual prioritization funnel that accelerates the discovery of novel therapeutic agents hidden within the plant kingdom, reducing the time and cost associated with traditional pharmacognosy.
Keywords: Biosynthetic Gene Clusters (BGCs), Genome Mining, Chemoinformatics, Structure Elucidation, Generative Adversarial Networks (GANs), Metabolomics
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