Exploration-NFT: Reinforcing Protein Dynamics Generators for Persistent Conformational Exploration

The scarcity of long-timescale molecular dynamics (MD) trajectories restricts protein dynamics generators to short-horizon supervision, biasing autoregressive rollouts toward local free-energy basins. We present Exploration-NFT, a backbone-agnostic forward-process reinforcement framework for persistent conformational exploration. Same-condition rollout groups define a shared, non-whitened time-lagged independent component analysis space; segment-wise new-cell increments provide temporal exploration rewards, while structural validity is regularized independently. DiffusionNFT maps these rewards to denoising updates without modifying native samplers or evaluating their likelihoods. We evaluate Exploration-NFT with BioKinema and DyneTrion on 100-ns ATLAS and 10-µs CATH1 and fast-folding benchmarks. Across both backbones and all three benchmarks, post-training improves valid diversity or reference-MD coverage while maintaining all reported local-validity scores above 99.96%. For BioKinema, ATLAS diversity increases 2.24×, and CATH1 and fast-folding coverage increases by 47% and 37%, respectively. At the maximum-coverage fast-folding checkpoint, all reported thermodynamic and kinetic discrepancies also decrease. Improvements beyond the training sequence-length range and at unseen temporal intervals further demonstrate transfer across conditions. These results establish forward-process reinforcement as an effective mechanism for expanding the conformational support of protein dynamics generators.