Abstract
Natural products (NPs) are invaluable resources for drug discovery, characterized by their intricate scaffolds and diverse bioactivities. AI drug discovery & design (AIDD) has emerged as a transformative approach for the rational structural modification of NPs. This review examines a variety of molecular generation models since 2020, focusing on their potential applications in two primary scenarios of NPs structure modification: modifications when the target is identified and when it remains unidentified. Most of the molecular generative models discussed herein are open-source, and their applicability across different domains and technical feasibility have been evaluated. This evaluation was accomplished by integrating a limited number of research cases and successful practices observed in the molecular optimization of synthetic compounds. Furthermore, the challenges and prospects of employing molecular generation modeling for the structural modification of NPs are discussed.