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.

翻译：分子动力学（MD）模拟仍是研究蛋白质动力学的金标准，但其计算成本限制了其在生物学相关时间尺度上的应用。近期生成模型在加速模拟方面展现出潜力，但由于架构限制、误差累积以及时空动力学建模不足，它们在长时程生成方面仍面临困难。我们提出了STAR-MD（面向分子动力学的时空自回归推演模型），这是一种可扩展的SE(3)等变扩散模型，能够在微秒时间尺度上生成物理合理的蛋白质轨迹。我们的核心创新在于采用具有联合时空注意力机制的因果扩散Transformer，该架构能有效捕捉复杂的时空依赖关系，同时避免现有方法的内存瓶颈问题。在标准ATLAS基准测试中，STAR-MD在所有指标上均达到最先进性能——相较于先前方法，在构象覆盖度、结构有效性和动态保真度方面均有显著提升。STAR-MD成功实现了稳定微秒尺度轨迹的外推生成，而基线方法在此任务中完全失效，且该模型在扩展推演过程中始终保持高质量结构特性。我们的综合评估揭示了当前模型在长时程生成方面的严重局限性，同时证明STAR-MD的联合时空建模能够实现生物学相关时间尺度上的稳健动力学模拟，为加速探索蛋白质功能开辟了新途径。