COMPUTER-AIDED NATURAL PRODUCT DRUG DISCOVERY: FROM VIRTUAL SCREENING TO LEAD OPTIMIZATION

Computer-aided drug discovery (CADD) has emerged as an indispensable tool in natural product research, dramatically accelerating the identification and optimization of bioactive compounds from complex natural sources. The integration of computational approaches with experimental natural product chemistry has transformed traditional discovery paradigms, enabling the rapid exploration of vast chemical space and the prioritization of promising candidates for experimental validation. This review comprehensively examines the application of CADD methodologies in natural product drug discovery, focusing on structure-based approaches (molecular docking and molecular dynamics) and ligand-based approaches (pharmacophore modeling and quantitative structure-activity relationship analysis). We critically evaluate the strengths and limitations of these computational tools in the context of natural product libraries, addressing challenges including conformational flexibility, stereochemical complexity, and the unique physicochemical properties of natural products. Furthermore, we assess the integration of early-stage ADMET prediction and the corroborative in-silico and experimental approaches that validate computational predictions. Despite significant advances, challenges including the accuracy of scoring functions, representation of natural product chemical space, and experimental validation requirements persist. This review identifies emerging trends and discusses strategies for optimizing CADD applications in natural product drug discovery.