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