Structure-based drug design (SBDD) relies on accurate knowledge of protein structure and ligand-binding conformations. However, most of the static conformations obtained by advanced methods such as structural biology and de novo protein folding algorithms often don't meet the needs for drug design. We introduce PackDock, a flexible docking method that combines "conformation selection" and "induced fit" mechanisms in a two-stage docking pipeline. The core module of this method is PackPocket, which uses a diffusion model to explore the side-chain conformation space in ligand binding pockets, both with or without a ligand. We evaluate our method using several tests that reflect real-world application scenarios. (1) Side-chain packing and Re-docking experiments validate the ability of PackDock to predict accurate side-chain conformations and ligand conformations. (2) Cross-docking experiments with apo and non-homologous ligand-induced holo structures align with real docking scenarios, demonstrating PackDock's practical value. (3) Docking experiments with hypothetical models show that PackPocket can potentially conduct SBDD starting from protein sequence information only. Additionally, we found that PackDock can identify key amino acid conformation changes, which may provide insights for lead compound optimization. We demonstrate PackDock can accurately predict the complex conformations in various application scenarios, by combining the conformation selection theory and the induced fit theory, and by using the ability of PackPocket to accurately predict the side chain conformations in the pocket region. We believe this method can improve the usability of existing structures, providing a new perspective for the SBDD community.

PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking