ABLE: All-Atom Biomolecular Trajectory Generation with Active-Ligand Enrichment

Molecular dynamics simulations provide an atomistic bridge between in silico modeling and experiments, but their high computational cost limits applications in high-throughput drug discovery. Recent generative models accelerate molecular dynamics by modeling large-lag transitions, yet models trained primarily on active protein–ligand complexes may inherit inductive biases that weaken active–decoy discrimination, limiting their ability to serve as general substitutes for physics-based simulations. To address this limitation, we introduce ABLE, an all-atom biomolecular trajectory generation framework designed to extend trajectory modeling toward virtual screening. At the post-training stage, ABLE incorporates an on-policy reinforcement learning procedure centered on a newly defined protein–ligand interfacial expansion rate. By quantifying the instantaneous entropy growth of a local interfacial distribution, this quantity provides the optimization signal that drives distinct dynamical behaviors for active ligands and decoys using only static cross-docked complex structures. Experiments demonstrate that ABLE achieves competitive trajectory generation performance on both proteins and protein–ligand complexes and, after reinforcement learning, delivers state-of-the-art early enrichment in virtual screening among the evaluated baselines.