用生成模型从分子动力学数据中学习多任务代理，实现模拟与设计新可能。
Generative Modeling of Molecular Dynamics Trajectories
- 基于轨迹片段条件生成，灵活适配多种任务
- 在四肽模拟中生成合理蛋白单体集合
- 适合分子模拟与逆向设计研究者
分子动力学（MD）是研究微观现象的强大工具，但其计算成本促使人们开发基于深度学习的替代模型。本文提出将分子轨迹的生成建模作为学习灵活多任务代理模型的新范式。通过在轨迹中适当帧上进行条件设定，该生成模型可适应前向模拟、过渡路径采样和轨迹上采样等多种任务。通过部分系统条件化并补全其余部分，我们首次展示了面向动态条件的分子设计。我们在四肽模拟中验证了全部功能，结果表明模型能生成合理的蛋白单体集合。整体而言，本工作展示了生成建模如何从MD数据中挖掘价值，推动现有方法甚至MD本身难以解决的下游任务。代码已公开于 https://github.com/bjing2016/mdgen。
原文摘要 · Abstract (English)
Molecular dynamics (MD) is a powerful technique for studying microscopic phenomena, but its computational cost has driven significant interest in the development of deep learning-based surrogate models. We introduce generative modeling of molecular trajectories as a paradigm for learning flexible multi-task surrogate models of MD from data. By conditioning on appropriately chosen frames of the trajectory, we show such generative models can be adapted to diverse tasks such as forward simulation, transition path sampling, and trajectory upsampling. By alternatively conditioning on part of the molecular system and inpainting the rest, we also demonstrate the first steps towards dynamics-conditioned molecular design. We validate the full set of these capabilities on tetrapeptide simulations and show that our model can produce reasonable ensembles of protein monomers. Altogether, our work illustrates how generative modeling can unlock value from MD data towards diverse downstream tasks that are not straightforward to address with existing methods or even MD itself. Code is available at https://github.com/bjing2016/mdgen.
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