Learning Implicit Bias in Generative Spaces for Accelerating Protein Dynamics Emulation

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 ~15× faster, and pairing it with refinement reaches the coverage up to ~37× faster while covering ~3× as many low-energy states. Code will be released soon.