Machine Learning for Predicting the Biological Activity of Phytochemicals: Bridging Traditional Natural Product Research and AI-Driven Drug Discovery

Abstract

Natural products have served as a rich source of therapeutic agents for centuries, with traditional systems of medicine such as Ayurveda, Siddha, and Traditional Chinese medicine providing valuable knowledge for modern drug discovery. Conventional natural product research involves plant selection, phytochemical extraction, compound isolation, structural characterization, and biological evaluation, a process that is often time-consuming, labor-intensive and expensive. Recent advances in Artificial Intelligence particularly Machine Learning (ML), have transformed this field by enabling rapid prediction of the biological activities of phytochemicals before extensive laboratory testing. Machine learning algorithms analyze large datasets of chemical structures, molecular descriptors and pharmacological information to predict activities such as anticancer, antimicrobial, antiviral, antioxidant and anti-inflammatory effects. Modern approaches including graph neural networks, explainable AI, and AI-assisted molecular docking, have significantly improved the accuracy and reliability of these predictions. Integration of public databases such as COCNUT, ChEMBL, PubChem and NPASS further enhances the identification of promising natural compounds and supports efficient lead optimization. Importantly, ML complements rather than replaces traditional natural product research. Ethnopharmacological knowledge continues to guide the selection of medicinal plants, while AI accelerates the prioritization of bioactive compounds for experimental validation. This synergistic approach reduces research time, minimizes cost and increases the likelihood of discovering novel plant-derived therapeutics. Future developments integrating machine learning with multi-omics technology and sustainable drug discovery are expected to further revolutionize pharmacognosy and pharmaceutical research. The convergence of traditional knowledge and AI-driven innovation represents a promising pathway toward faster, more efficient and evidence-based natural product drug discovery.