Structure-informed machine learning for drug discovery: a task-centric perspective

Abstract Advances in protein structure prediction have transformed the landscape of structure-based drug discovery, enabling deep learning models to incorporate spatial constraints into the design of target-specific ligands. This review provides a comprehensive synthesis of structure-aware molecular modeling from a task-centric perspective, focusing on binding pocket identification, interaction prediction, pose estimation, and complex modeling. We highlight the technological evolution from traditional docking and scoring frameworks toward geometry-informed deep learning architectures that encode protein structures via surface geometry, equivariant representations, and multi-modal embeddings. Special attention is given to recent progress in structure-conditioned molecular generation. We classify generative approaches into four core strategies: sequence-based generation with 3D conditioning, fragment-based linking and growing, graph-based generation under structural constraints, and 3D coordinate–based generation including diffusion models. Each paradigm balances chemical validity, spatial fidelity, and computational tractability in distinct ways, with diffusion-based and point cloud models emerging as powerful tools for synthesizing pocket-complementary molecules in full 3D space. We also discuss the emergence of co-folding models, which unify protein folding and ligand binding into a single predictive framework, bridging the gap between sequence-level learning and structural resolution. Finally, we examine the key challenges of data scarcity, generalization, and multi-objective control, and outline future directions toward scalable, interpretable, and physically plausible generation pipelines. By tracing how structural knowledge is reshaping AI-driven drug design, this review aims to provide both a conceptual roadmap and practical insight into next-generation molecular modeling.