ARTIFICIAL INTELLIGENCE AND MULTI-OMICS IN ETHNOPHARMACOLOGY: ADVANCING PRECISION DRUG DISCOVERY FROM TRADITIONAL MEDICINE

Dr. Pasham Uma*, Swathi Mudavath, Muvvala Sudhakar

Ethnopharmacology has played a pivotal role in drug discovery by documenting traditional knowledge of medicinal plants and their therapeutic applications. Numerous clinically important drugs, including aspirin, artemisinin, quinine, and paclitaxel, originated from ethnopharmacological investigations. However, conventional approaches are often constrained by time-intensive phytochemical analyses, limited mechanistic understanding, poor standardization, and complex validation processes. Recent advances in artificial intelligence (AI) and multi-omics technologies offer transformative opportunities to overcome these limitations and accelerate evidence-based natural product research. This review examines the integration of AI with genomics, transcriptomics, proteomics, metabolomics, and microbiomics to improve the identification, characterization, and validation of bioactive phytochemicals. Machine learning, deep learning, natural language processing, and computer-aided drug design facilitate the prediction of biological activity, molecular targets, pharmacokinetic properties, and toxicity profiles, thereby streamlining the drug discovery pipeline. Complementary approaches such as network pharmacology, molecular docking, molecular dynamics simulations, and systems biology enable comprehensive analysis of the multi-component, multi-target mechanisms of herbal medicines. The convergence of AI and multi-omics bridges traditional medicinal knowledge with modern precision medicine by enabling the discovery of novel therapeutic candidates and personalized treatment strategies. Furthermore, this review highlights current challenges, including data standardization, experimental validation of computational predictions, ethical considerations, explainable AI, and regulatory compliance. Addressing these challenges through interdisciplinary collaboration and robust analytical frameworks will enhance the reliability, reproducibility, and clinical translation of ethnopharmacological research. Overall, the integration of AI and multi-omics represents a promising paradigm for advancing precision phytomedicine and accelerating the development of safe and effective plant-derived therapeutics.