Modeling protein–ligand interactions for drug discovery in the era of deep learning

drug design with deep generative models, and (5) sequence-based methods for interaction prediction and drug discovery. In this review, we provide a focused overview of these advances, highlight emerging strategies for their integration, examine ongoing challenges, and outline future directions. We argue that bridging physics-based and data-driven approaches not only improves predictive power and efficiency, but also enables exploration of the vast chemical and biological spaces central to modern drug discovery.