发表机构
School of Computer Science and Engineering, Sun Yat-sen University; Shenzhen Loop Area Institute; Pengcheng Laboratory; Guangdong Provincial Key Laboratory of Computational Science, Sun Yat-sen University（中山大学计算机学院; 深圳河套学院; 鹏城实验室; 中山大学广东省计算科学重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对肽动力学中稀有过渡事件难以学习的问题，提出多尺度时间框架PepTIDE，通过连续步幅窗口和自适应物理时间嵌入，结合随机插值流与不变点注意力，在PepMD和环状肽基准上达到最先进性能。
AI 中文摘要
肽在生物分子动力学中占据了一个特别具有挑战性的领域。短肽缺乏稳定的折叠核心，并占据广泛的构象系综，而环化则增加了环闭合、非局部残基耦合和立体化学多样性。深度生成模型在直接模拟分子动力学轨迹方面取得了显著进展，但每个模型都是在固定间隔切割的窗口上训练的，因此以固定的时间分辨率观察过程。更根本的是，没有一个模型将该间隔作为输入，因此窗口所跨越的物理时间从未被表示，使得肽在亚稳态之间跨越的稀疏帧难以学习。在此，我们介绍了PepTIDE，一个用于肽轨迹生成的多尺度时间框架。每个训练窗口的步幅从连续范围内抽取，因此一个模型可以在多个时间分辨率下观察相同的动力学，并且自适应物理时间嵌入编码窗口的相对间隔以及每个帧的绝对时间，使得共享速度场具有间隔感知性。所有帧通过随机插值流联合生成，并且对感知的不变点注意力捕捉线性和环状肽的几何和拓扑。在PepMD和50系统环状肽基准上，PepTIDE达到了最先进的分布一致性和结构有效性。按时间顺序读取，其轨迹恢复了这些稀疏的过渡帧，并更忠实地再现了状态间通量，确认了建模多个时间尺度是使这些稀有事件可访问的原因。
英文摘要
Peptides occupy a particularly challenging regime of biomolecular dynamics. Short peptides lack a stable folded core and populate broad conformational ensembles, while cyclization adds ring closure, non-local residue coupling, and stereochemical diversity. Deep generative models have made remarkable progress in emulating molecular-dynamics trajectories directly, yet each is trained on windows cut at a fixed interval and therefore observes the process at a fixed temporal resolution. More fundamentally, none takes that interval as an input, so the physical time a window spans is never represented, leaving the sparse frames on which a peptide crosses between metastable states difficult to learn. Here, we introduce PepTIDE, a multi-scale temporal framework for peptide trajectory generation. The stride of each training window is drawn from a continuous range, so one model observes the same dynamics at many temporal resolutions, and an Adaptive Physical-Time Embedding encodes the relative interval of a window together with the absolute time of each frame, making the shared velocity field interval-aware. All frames are generated jointly through a stochastic-interpolant flow, and pair-aware invariant point attention captures the geometry and topology of both linear and cyclic peptides. On PepMD and a 50-system cyclic-peptide benchmark, PepTIDE reaches state-of-the-art distribution agreement and structural validity. Read in temporal order, its trajectories recover these sparse transition frames and reproduce inter-state fluxes more faithfully, confirming that modeling multiple temporal scales is what makes these rare events accessible.
