Zhang Jun, Chen Dechin, Xia Yijie, Huang Yu-Peng, Lin Xiaohan, Han Xu, Ni Ningxi, Wang Zidong, Yu Fan, Yang Lijiang, Yang Yi Isaac, Gao Yi Qin
Changping Laboratory, Beijing 102200, China.
Institute of Systems Biology, Shenzhen Bay Laboratory, 518055 Shenzhen, China.
J Chem Theory Comput. 2023 Jul 25;19(14):4338-4350. doi: 10.1021/acs.jctc.3c00214. Epub 2023 Jun 26.
Molecular simulations, which simulate the motions of particles according to fundamental laws of physics, have been applied to a wide range of fields from physics and materials science to biochemistry and drug discovery. Developed for computationally intensive applications, most molecular simulation software involves significant use of hard-coded derivatives and code reuse across various programming languages. In this Review, we first align the relationship between molecular simulations and artificial intelligence (AI) and reveal the coherence between the two. We then discuss how the AI platform can create new possibilities and deliver new solutions to molecular simulations, from the perspective of algorithms, programming paradigms, and even hardware. Rather than focusing solely on increasingly complex neural network models, we introduce various concepts and techniques brought about by modern AI and explore how they can be transacted to molecular simulations. To this end, we summarized several representative applications of molecular simulations enhanced by AI, including from differentiable programming and high-throughput simulations. Finally, we look ahead to promising directions that may help address existing issues in the current framework of AI-enhanced molecular simulations.
分子模拟根据物理基本定律模拟粒子运动,已应用于从物理、材料科学到生物化学和药物发现等广泛领域。由于是为计算密集型应用而开发的,大多数分子模拟软件大量使用硬编码导数并在各种编程语言之间进行代码复用。在本综述中,我们首先梳理分子模拟与人工智能(AI)之间的关系,并揭示两者之间的连贯性。然后,我们从算法、编程范式甚至硬件的角度讨论AI平台如何为分子模拟创造新的可能性并提供新的解决方案。我们并非只关注日益复杂的神经网络模型,而是引入现代AI带来的各种概念和技术,并探索如何将它们应用于分子模拟。为此,我们总结了几个由AI增强的分子模拟的代表性应用,包括可微编程和高通量模拟。最后,我们展望了可能有助于解决当前AI增强分子模拟框架中现有问题的有前景的方向。