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We present PackDock, a framework that integrates deep learning and physics-based modeling to represent protein\u2013ligand interactions. PackDock\u2019s core, PackPocket, uses diffusion models to sample diverse binding pocket conformations and predict ligand-induced changes. We validate PackDock through side-chain packing, redocking, and cross-docking experiments, demonstrating its ability to address protein flexibility challenges. In a real-world application, PackDock identified nanomolar affinity compounds with unreported scaffolds for the protein of interest. Additionally, it revealed key amino acid conformational changes, offering insights into protein\u2013ligand interactions. 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