用扩散模型生成微秒级蛋白质动态，突破传统模拟的时长瓶颈
Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics
- 基于时空因果注意力的SE(3)等变扩散模型，高效建模空间时间依赖关系
- 在ATLAS基准上实现最先进的构象覆盖与结构保真度，成功生成微秒级稳定轨迹
- 适合需要长期蛋白质动力学模拟的研究者，尤其在药物设计中价值突出
分子动力学（MD）模拟仍是研究蛋白质动态的金标准，但其计算成本限制了对生物相关时间尺度的探索。近年来生成模型展现出加速模拟的潜力，但在长时间跨度生成方面受限于架构缺陷、误差累积及对时空动态建模不足。本文提出STAR-MD（Spatio-Temporal Autoregressive Rollout for Molecular Dynamics），一种可扩展的SE(3)等变扩散模型，可在微秒尺度生成物理上合理的蛋白质轨迹。核心创新在于采用带有联合时空注意力的因果扩散变压器，有效捕捉复杂时空依赖性，同时避免现有方法的内存瓶颈。在标准ATLAS基准上，STAR-MD在所有指标上均达到最新水平，显著提升构象覆盖率、结构有效性与动态保真度。相比基线方法，它能成功外推生成稳定的微秒级轨迹，全程保持高结构质量。全面评估揭示当前模型在长时序生成中的严重局限，而STAR-MD的联合时空建模能力为生物相关时间尺度的稳健动力学模拟开辟新路径，推动蛋白质功能的加速探索。
原文摘要 · Abstract (English)
Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics, but their computational cost limits access to biologically relevant timescales. Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation due to architectural constraints, error accumulation, and inadequate modeling of spatio-temporal dynamics. We present STAR-MD (Spatio-Temporal Autoregressive Rollout for Molecular Dynamics), a scalable SE(3)-equivariant diffusion model that generates physically plausible protein trajectories over microsecond timescales. Our key innovation is a causal diffusion transformer with joint spatio-temporal attention that efficiently captures complex space-time dependencies while avoiding the memory bottlenecks of existing methods. On the standard ATLAS benchmark, STAR-MD achieves state-of-the-art performance across all metrics--substantially improving conformational coverage, structural validity, and dynamic fidelity compared to previous methods. STAR-MD successfully extrapolates to generate stable microsecond-scale trajectories where baseline methods fail catastrophically, maintaining high structural quality throughout the extended rollout. Our comprehensive evaluation reveals severe limitations in current models for long-horizon generation, while demonstrating that STAR-MD's joint spatio-temporal modeling enables robust dynamics simulation at biologically relevant timescales, paving the way for accelerated exploration of protein function.
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