{"ID":2921105,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-04T07:41:34.29888543Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01833","arxiv_id":"2606.01833","title":"Learning Implicit Bias in Generative Spaces for Accelerating Protein Dynamics Emulation","abstract":"Generative emulators of protein dynamics produce plausible trajectories at a fraction of the cost of molecular dynamics, but they inherit their training distribution and tend to revisit known states rather than reach rare ones under long-horizon extrapolation. Inspired by classical enhanced sampling, we introduce an implicit, history-dependent bias in the generative space of a pretrained emulator. Specifically, a history-aware score estimator augments the frozen emulator with a distance-weighted bias that steers reverse-time sampling away from previously generated structures, regularized by an environment-support term. To preserve structural validity at long horizons, a score-based refinement step re-projects drifted samples onto the data manifold using the frozen emulator. Our experiments demonstrate that the method (i) raises diversity by $35\\%$ on DynamicPDB-80; (ii) on $12$ zero-shot Fast-Folding proteins, the learned bias alone reaches the unbiased emulator's coverage up to ${\\sim}15\\times$ faster, and pairing it with refinement reaches the coverage up to ${\\sim}37\\times$ faster while covering ${\\sim}3\\times$ as many low-energy states. Code will be released soon.","short_abstract":"Generative emulators of protein dynamics produce plausible trajectories at a fraction of the cost of molecular dynamics, but they inherit their training distribution and tend to revisit known states rather than reach rare ones under long-horizon extrapolation. Inspired by classical enhanced sampling, we introduce an im...","url_abs":"https://arxiv.org/abs/2606.01833","url_pdf":"https://arxiv.org/pdf/2606.01833v1","authors":"[\"Kaihui Cheng\",\"Zhiqiang Cai\",\"Wenkai Xiang\",\"Zhihang Hu\",\"Siyu Zhu\",\"Tzuhsiung Yang\",\"Yuan Qi\"]","published":"2026-06-01T07:46:28Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}