Conditional molecular dynamics refinement for protein-ligand affinity prediction.

Protein-ligand affinity prediction is fundamental to structure-based virtual screening and lead discovery. However, most existing methods rely on a single static conformation of the complex and exhibit high sensitivity to pose uncertainty and conformational noise. To address this limitation, CMD-PLA is proposed as a dynamics-aware framework for protein-ligand affinity prediction. Within this framework, pocket-conditioned molecular dynamics refinement is performed, and the conformational evolution of the ligand is explicitly modeled as a pocket-dependent dynamical process rather than an isolated static update. Furthermore, a dual-view atomic representation is adopted to separately capture the intra-molecular covalent structure and the inter-molecular interaction geometry. Global representations of the ligand and the pocket are also incorporated to complement the local geometric modeling. Experimental results demonstrate that CMD-PLA achieves robust performance across various settings. The provided case study further illustrates the interpretability of the model.