用状态空间模型生成原子级生物分子动态轨迹，速度快且能捕捉长时依赖。
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
- 基于配对形式器的状态转移机制建模长期时间依赖关系。
- 在蛋白单体与复合物系统上均达到当前最佳生成效果。
- 适合需要高效模拟生物分子运动的药物发现研究者。
理解生物分子的动力学行为对于揭示生物学功能和促进药物发现至关重要。虽然分子动力学（MD）模拟提供了研究这些动态的严格物理基础，但在长时间尺度下仍计算成本高昂。相反，近期的深度生成模型虽加速了构象生成，但通常无法建模时间关系，或仅适用于单体蛋白。为此，我们提出ATMOS，一种基于状态空间模型（SSM）的新颖生成框架，用于生成生物分子系统的原子级MD轨迹。ATMOS结合基于配对形式器的状态转移机制以捕捉长程时间依赖性，并采用基于扩散的模块以自回归方式解码轨迹帧。ATMOS在来自PDB的晶体结构及大规模MD模拟数据集mdCATH和MISATO中的构象轨迹上进行训练。实验表明，ATMOS在蛋白单体及蛋白-配体复合系统上的构象轨迹生成上均达到当前最优性能。通过实现原子级运动轨迹的高效推理，本工作为建模生物分子动力学奠定了有力基础。
原文摘要 · Abstract (English)
Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Conversely, recent deep generative models accelerate conformation generation but are typically either failing to model temporal relationship or built only for monomeric proteins. To bridge this gap, we introduce ATMOS, a novel generative framework based on State Space Models (SSM) designed to generate atom-level MD trajectories for biomolecular systems. ATMOS integrates a Pairformer-based state transition mechanism to capture long-range temporal dependencies, with a diffusion-based module to decode trajectory frames in an autoregressive manner. ATMOS is trained across crystal structures from PDB and conformation trajectory from large-scale MD simulation datasets including mdCATH and MISATO. We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and complex protein-ligand systems. By enabling efficient inference of atomic trajectory of motions, this work establishes a promising foundation for modeling biomolecular dynamics.
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