Sahrmann Patrick G, Voth Gregory A
Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL 60637, USA.
Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL 60637, USA.
Curr Opin Struct Biol. 2025 Feb;90:102972. doi: 10.1016/j.sbi.2024.102972. Epub 2025 Jan 2.
Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which 'coarse-grain' electronic structure have inspired similar applications to the thermodynamic coarse-graining of chemical and biological systems. In this review, we discuss the current viability and challenges in using machine-learning methods to represent coarse-grained force-fields, as well as the utility of machine-learning in various aspects of coarse-grained modeling.
机器学习方法在计算化学领域已备受关注，成为分子建模与分析的一种可行方法。近期利用神经网络学习对电子结构进行“粗粒化”的原子力场取得的成功，激发了在化学和生物系统热力学粗粒化方面的类似应用。在本综述中，我们讨论了使用机器学习方法表示粗粒化力场的当前可行性和挑战，以及机器学习在粗粒化建模各方面的效用。
