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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2606.01833 |
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| _version_ | 1866914622138744832 |
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| author | Cheng, Kaihui Cai, Zhiqiang Xiang, Wenkai Hu, Zhihang Zhu, Siyu Yang, Tzuhsiung Qi, Yuan |
| author_facet | Cheng, Kaihui Cai, Zhiqiang Xiang, Wenkai Hu, Zhihang Zhu, Siyu Yang, Tzuhsiung Qi, Yuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01833 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Implicit Bias in Generative Spaces for Accelerating Protein Dynamics Emulation Cheng, Kaihui Cai, Zhiqiang Xiang, Wenkai Hu, Zhihang Zhu, Siyu Yang, Tzuhsiung Qi, Yuan Machine Learning Artificial Intelligence 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. |
| title | Learning Implicit Bias in Generative Spaces for Accelerating Protein Dynamics Emulation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2606.01833 |