Structure-aware artificial intelligence for next-generation drug discovery: from protein–ligand modeling to generative biomolecular design
TL;DR
This review surveys state-of-the-art methods across drug–target interaction prediction, protein–ligand complex modeling and docking, de novo molecular generation, and biomolecule design, examining the convergence of docking, structure prediction, and molecular generation within co-folding and diffusion-based frameworks.
Abstract
Abstract Recent advances in protein structure determination and prediction, large-scale structural databases, and artificial intelligence have reshaped structure-based drug discovery. Structure-aware artificial intelligence models integrate molecular representation learning with three-dimensional protein information to model interactions, predict complex structures and binding poses, and generate novel molecules. In this review, we follow this paradigm along a continuum from protein–ligand modeling to the de novo design of biomolecular binders. We first outline the molecular and protein representations that render structures computable, together with the growing collection of structural data resources. We then survey state-of-the-art methods across drug–target interaction prediction, protein–ligand complex modeling and docking, de novo molecular generation, and biomolecule design, examining the convergence of docking, structure prediction, and molecular generation within co-folding and diffusion-based frameworks. Despite these advances, prospective experimental validation remains scarce, and persistent limitations such as biased structural coverage, limited and ambiguous negative supervision, fragmented benchmarking, and insufficient mechanistic interpretability continue to constrain real-world utility. Progress in data quality and supervision design, evaluation rigor, and design-relevant interpretability will be essential to translate methodological innovation into practical impact.