Transforming the Landscape of Drug Innovation in AI Era: AlphaFold 3 and Beyond

Abstract

ABSTRACT The traditional drug discovery and development (DDD) pipeline has long been characterized by inefficiency and high costs. The rapid evolution of artificial intelligence (AI), especially AI‐based toolkits for protein structure and interaction prediction, exemplified by AlphaFold 3 (AF3), promises to reshape the landscape of drug innovation. AF3 advances structural biology by predicting protein structures and their interactions with a range of biomolecules, including nucleic acids, small‐molecule ligands, and ions, potentially changing the conventional DDD paradigm, although prediction accuracy varies substantially across these categories. This perspective examines the impact of AF3 on drug target identification and validation, hit identification, lead optimization, and personalized clinical decision‐making, while addressing the technical challenges, ethical considerations, and governance implications. Solutions to overcome current limitations are proposed to advance the development of novel therapeutics.