AI-ASSISTED PHARMACOGNOSY: INTEGRATING ARTIFICIAL INTELLIGENCE WITH NATURAL PRODUCT DRUG DISCOVERY

Abstract

Artificial Intelligence (AI) is rapidly transforming pharmacognosy by enabling data-driven discovery of bioactive natural compounds from plants, microorganisms, and marine organisms. Traditional pharmacognosy relies on ethnomedicinal knowledge, phytochemical isolation, and biological screening; however, these approaches are time-consuming, costly, and often inefficient due to structural redundancy and chemical complexity inherent in natural products. The integration of AI methodologies such as machine learning (ML), deep learning (DL), natural language processing (NLP), and generative models has significantly enhanced the speed and precision of natural product drug discovery. AI-assisted pharmacognosy facilitates predictive modeling of phytochemical bioactivity, virtual screening of compound libraries, dereplication of known molecules, and multi-omics data integration for target identification. Furthermore, AI enables the interpretation of complex metabolomic and spectral datasets, improving compound annotation and accelerating lead identification. The convergence of computational intelligence with pharmacognosy is also reshaping ethnopharmacology by mining large-scale textual databases to uncover hidden therapeutic knowledge from traditional medicine systems. Despite these advances, challenges such as limited curated datasets, poor standardization of phytochemical information, model interpretability issues, and lack of experimental validation frameworks remain significant barriers. Future directions include integration of explainable AI (XAI), digital twins of medicinal plants, federated learning systems, and generative chemistry models for de novo drug design. Overall, AI-assisted pharmacognosy represents a paradigm shift from empirical discovery to predictive, computationally driven natural product research, significantly improving efficiency, scalability, and translational success in drug development pipelines.