Making the Most of Molecular Dynamics Simulations for Binding Affinity Prediction

Molecular dynamics (MD) simulations provide rich conformational information for protein–ligand binding affinity prediction, yet existing approaches typically treat simulated conformation samples independently or compress trajectory-level dynamics into static representations, leaving fine-grained interaction dynamics underexploited. In this work, we introduce DrugDyn, a unified framework that makes more effective use of molecular dynamics simulations for protein–ligand binding affinity prediction. DrugDyn integrates Geometry-Aware Spatiotemporal Attention (GASA) to capture geometry-dependent interactions within individual conformations and their dependencies across conformations, together with Multi-Cardinality Contrastive Learning (MCCL) to learn coherent and discriminative complex-level representations from different subsets of the conformational ensemble. Experiments across five backbone architectures under both standard PDBbind core-set evaluation and cross-protein LBA splits demonstrate consistent gains in both predictive accuracy and ranking consistency. On the challenging LBA-30 split, DrugDyn reduces RMSE by 13.8% and improves Kendall's $\tau$ by 9.3% on average over the baselines.