用马尔可夫状态模型生成蛋白质动态，速度提升超百倍。
MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models
- 基于马尔可夫状态模型定义离散状态，学习跨状态转移
- 相比传统模拟快200倍以上，能高效生成长时序轨迹
- 适用于多种蛋白结构，尤其适合研究构象变化与泛化能力
分子动力学（MD）是研究蛋白质功能的强大计算工具，但精细积分需求和生物大分子事件的长时尺度导致其计算成本高昂。为解决此问题，已有多种生成模型被提出以低成本生成替代轨迹，但这些方法通常学习固定滞后转移密度，使训练信号受频繁但信息量低的转移主导。本文提出一类新生成模型——MSM模拟器，通过学习基于底层马尔可夫状态模型（MSM）定义的离散状态间转移来建模。我们具体实现为马尔可夫空间流匹配（MarS-FM），其采样速度比隐式或显式溶剂MD模拟快超过两个数量级。我们在多种蛋白质结构域（最大达500残基）上评估了MarS-FM重现MD统计特性能力，涵盖均方根偏差（RMSD）、回转半径和二级结构含量等结构可观测量，并严格确保训练集与测试集序列差异显著以检验泛化性。在所有指标上，MarS-FM均显著优于现有方法。
原文摘要 · Abstract (English)
Molecular Dynamics (MD) is a powerful computational microscope for probing protein functions. However, the need for fine-grained integration and the long timescales of biomolecular events make MD computationally expensive. To address this, several generative models have been proposed to generate surrogate trajectories at lower cost. Yet, these models typically learn a fixed-lag transition density, causing the training signal to be dominated by frequent but uninformative transitions. We introduce a new class of generative models, MSM Emulators, which instead learn to sample transitions across discrete states defined by an underlying Markov State Model (MSM). We instantiate this class with Markov Space Flow Matching (MarS-FM), whose sampling offers more than two orders of magnitude speedup compared to implicit- or explicit-solvent MD simulations. We benchmark Mars-FM ability to reproduce MD statistics through structural observables such as RMSD, radius of gyration, and secondary structure content. Our evaluation spans protein domains (up to 500 residues) with significant chemical and structural diversity, including unfolding events, and enforces strict sequence dissimilarity between training and test sets to assess generalization. Across all metrics, MarS-FM outperforms existing methods, often by a substantial margin.
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