DuET-MD: Training-Free Rare-Event Steering for Frozen Molecular Dynamics Surrogates

Protein dynamics is central to understanding biological function, while atomistic molecular dynamics simulation, the gold-standard tool for characterizing protein dynamics, is too costly to observe rare transitions across high free-energy barriers. Recent MD surrogates accelerate trajectory generation by learning ps-to-ns finite-lag transitions, but faster propagation alone does not solve rare-event sampling: their default rollouts are concentrated around high-probability transitions learned from reference trajectories, leaving rare-event sampling unresolved. Applying conventional enhanced-sampling biases to frozen generative transition models is also non-trivial. We introduce DuET-MD, a training-free inference-time framework for steering frozen iterative MD surrogates. DuET-MD couples two particle processes across distinct clocks. Within each next-frame generation, inner particles use guidance and intermediate resampling to discover promising transitions before frame completion. Across physical time, an outer particle process uses the selected frame together with an estimated local normalizer to properly weight and resample competing trajectory prefixes. This enables path conditioning without retraining the surrogate. Across protein-folding and cryptic-pocket-opening tasks, DuET-MD increases the recovery of rare-transition candidates over default surrogate rollouts under matched finite sampling budgets. We further evaluate geometric validity and mechanistic ordering not encoded in the steering objective. DuET-MD thus extends accelerated MD surrogates from fast rollout to training-free rare-transition sampling.