Chapter 6: AI in Natural Product Drug Discovery
Synopsis
Author
Mrs. Sreedevi Kudaravalli,
Associate Professor, Department of Pharmaceutical Biotechnology, Sultan-ul-Uloom College of Pharmacy, Banjara Hills, Hyderabad, Telangana, India
Abstract
Revitalizing the pipeline for natural product drug discovery requires overcoming the historical challenges of structural complexity and isolation difficulty through computational innovation. Artificial Intelligence catalyzes this process by constructing and screening massive virtual libraries of natural products, utilizing deep learning to predict binding affinities against novel biological targets. This approach facilitates target deconvolution, identifying the precise molecular mechanisms of bioactive extracts through network pharmacology and inverse docking strategies that map ligand-protein interactions. Generative models further enhance this by designing de novo drug candidates inspired by natural scaffolds, utilizing reinforcement learning to optimize them for improved bioavailability and reduced toxicity. In the context of traditional medicine, AI modernizes the use of herbal formulations by predicting potential herb-drug interactions through the mining of biomedical literature and clinical data, thereby preventing adverse clinical events. Optimization algorithms simulate complex polyherbal mixtures to identify synergistic combinations where ingredients amplify each other's therapeutic effects while minimizing off-target toxicity. This rational design process bridges the gap between ancient botanical wisdom and modern evidence-based pharmacotherapy, delivering standardized, efficacious, and safe natural health products that meet the rigorous demands of modern healthcare systems.
Keywords: Network Pharmacology, Inverse Docking, Scaffold Hopping, Herb-Drug Interactions (HDIs), Virtual Libraries, Synergistic Formulations
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