Structure‐Based Drug Discovery with Deep Learning

Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of the deep learning efforts in drug discovery have focused on ligand‐based approaches, structure‐based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding‐mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep‐learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a renaissance in structure‐based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure‐based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead. Artificial intelligence (AI) in the form of deep learning shows great promise for drug discovery, from predicting protein structures to designing ligands de novo. AI for structure‐based drug discovery has the potential to tackle unsolved challenges, such as predicting ligand affinity for unexplored protein targets and navigating uncharted regions in the chemical space. This review delves into cutting‐edge deep‐learning concepts for structure‐based drug discovery and explores the exciting applications, opportunities, and challenges that lie ahead.