arXiv：cs.LG（机器学习，全量分类）· Weilong Chen, Nuno Costa, Julija Zavadlav·· 9 小时前AI 评分34
SupraTITO：面向超分子系统的可迁移生成式分子动力学
SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems
AI 导读
SupraTITO 是一个面向超分子系统的可迁移生成式分子动力学（GenMD）框架，通过肽自组装任务进行验证。它学习以肽序列、分子拓扑和周期性几何为条件的可迁移隐式转移算子（TITO），使构型能跨越远长于 MD 积分步长的物理时间间隔传播。在二肽基准上，SupraTITO 可泛化到未见序列，复现序列依赖的结构与动力学，并在长程推演中保持分子完整性，其动力学还能泛化到训练中未包含的稀溶液浓度。
正文
Abstract:Peptide sequence governs both the structures formed through supramolecular assembly and the dynamics by which they emerge, but predicting either requires resolving slow collective processes among many interacting molecules. Molecular dynamics (MD) provides microscopic insight into these processes, yet the long timescales of assembly and the vast peptide sequence space make systematic exploration computationally demanding. We introduce SupraTITO, a transferable generative molecular dynamics (GenMD) framework for supramolecular systems, demonstrated through peptide self-assembly. SupraTITO learns transferable implicit transfer operators (TITO) conditioned on peptide sequence, molecular topology, and periodic geometry, allowing configurations to be propagated over physical intervals much longer than an MD integration step. On a comprehensive dipeptide benchmark, SupraTITO generalizes to held-out sequences and reproduces sequence-dependent structures and dynamics while maintaining molecular integrity over long rollouts. Compared with direct ensemble prediction trained on the same trajectory data, SupraTITO more accurately reproduces assembly structures while also resolving their temporal evolution. The learned dynamics generalize across peptide concentrations, including dilute conditions not represented during training. These results extend transferable GenMD to collective dynamics in periodic supramolecular systems and provide a foundation for modeling related processes beyond peptide assembly.
| Subjects: | Chemical Physics (physics.chem-ph); Soft Condensed Matter (cond-mat.soft); Machine Learning (cs.LG); Biological Physics (physics.bio-ph) |
| Cite as: | arXiv:2610.01381 [physics.chem-ph] |
| (or arXiv:2610.01381v1 [physics.chem-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01381 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Weilong Chen [view email]
[v1]
Thu, 1 Oct 2026 09:46:15 UTC (8,875 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org