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Molecular docking, a key technique in structure-based drug design, playspivotal roles in protein-ligand interaction modeling, hit identification andoptimization, in which accurate prediction of protein-ligand binding mode isessential. Conventional docking approaches perform well in redocking tasks withknown protein binding pocket conformation in the complex state. However, inreal-world docking scenario without knowing the protein binding conformationfor a new ligand, accurately modeling the binding complex structure remainschallenging as flexible docking is computationally expensive and inaccurate.Typical deep learning-based docking methods do not explicitly consider proteinside chain conformations and fail to ensure the physical plausibility anddetailed atomic interactions. In this study, we present DiffBindFR, a full-atomdiffusion-based flexible docking model that operates over the product space ofligand overall movements and flexibility and pocket side chain torsion changes.We show that DiffBindFR has higher accuracy in producing native-like bindingstructures with physically plausible and detailed interactions than availabledocking methods. Furthermore, in the Apo and AlphaFold2 modeled structures,DiffBindFR demonstrates superior advantages in accurate ligand binding pose andprotein binding conformation prediction, making it suitable for Apo andAlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexibledocking tool for modeling accurate protein-ligand binding structures.

DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking