Artificial Intelligence–Driven Natural Product Drug Discovery: Integrating Machine Learning with Metabolite Databases

Natural products have been a key driver of drug discovery through their diverse chemical nature and wide range of biological activities. However, conventional drug discovery based on natural products is often limited by issues such as re-discovery of known compounds, complicated structure determination, constrained scalability, and lengthy development times.The integration of artificial intelligence (AI), machine learning (ML), and extensive metabolite databases is changing the way drug discovery works by allowing data-driven, predictive, and high-throughput discovery strategies. This chapter reviews the impact of machine learning algorithms combined with natural product and metabolite databases on various stages of drug discovery such as dereplication, bioactivity prediction, virtual screening, structure-activity relationship analysis, and mechanism of action inference.Ocean and microbial natural products are primarily focused on as typical examples of chemically rich resources that are less explored. Besides that, the chapter outlines present issues regarding data quality, model interpretability, and database interoperability and points out potential future developments like generative AI and autonomous discovery platforms. Altogether, this integration illustrates the way AI-driven informatics frameworks are revolutionizing natural product research to be quicker, more efficient, and more environmentally friendly pharmaceutical discovery.