Dynamics-Driven Protein–Ligand Binding Affinity Prediction via Conformational Flexibility Modeling

Protein-ligand binding affinity (PLBA) prediction has benefited substantially from structural information, yet available structures typically provide only static snapshots of the binding state, limiting the characterization of conformational and energetic evolution during molecular recognition. This limitation becomes particularly pronounced when binding spans multiple conformational states that reshape local interactions and energetics, leading to state-dependent variations in binding strength. To address this, we propose D-PLBA, a dynamics-driven framework that integrates dynamic pocket-ligand trajectory prediction into PLBA prediction. D-PLBA predicts motion trajectories by modeling pocket motion, ligand rigid-body displacement, and torsional deformation, while estimating the corresponding binding interaction energies. The resulting conformations and energies are then incorporated into the affinity predictor to jointly characterize the spatiotemporal evolution of binding structures and interactions. Extensive experiments demonstrate that D-PLBA substantially outperforms state-of-the-art methods across most evaluated metrics on all benchmarks, while reducing dynamic multi-conformation inference time to approximately 1% of that required by existing approaches. Our code and datasets can be found at: https://anonymous.4open.science/r/anonymous-703E.