Molecular dynamics is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to molecular dynamics, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD samples rare events at a higher rate than traditional MD, however critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

翻译：分子动力学是现代结构生物学探索大分子结构与功能的主要计算方法。玻尔兹曼生成器被提出作为分子动力学的替代方案，通过将分子系统随时间积分替换为生成式神经网络的训练来实现。这种基于神经网络的分子动力学方法在稀有事件采样速率上优于传统分子动力学，然而玻尔兹曼生成器在理论完备性与计算可行性方面的关键缺陷显著降低了其实用性。本文建立了一种数学基础以突破这些障碍；我们证明玻尔兹曼生成器方法足以在特定应用中替代传统分子动力学处理复杂大分子（如蛋白质），并为利用神经网络探索分子能量景观提供了综合工具包。