Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA. Electronic address: https://twitter.com/@harshbhatia85.
Physical and Life Sciences (PLS) Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA.
Curr Opin Struct Biol. 2023 Jun;80:102569. doi: 10.1016/j.sbi.2023.102569. Epub 2023 Mar 24.
Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.
多尺度建模在结构生物学中有着悠久的应用历史，因为计算生物学家努力克服原子分子动力学的时间和长度尺度限制。当代机器学习技术，如深度学习，推动了几乎每个科学和工程领域的进步，并且正在使传统的多尺度建模观念重新焕发生机。深度学习在从精细模型中提取信息的各种方法中取得了成功，例如构建替代模型和指导粗粒势的开发。然而，它在多尺度建模中的最强大用途可能是定义潜在空间，从而能够有效地探索构象空间。机器学习和多尺度模拟与现代高性能计算的融合，有望为结构生物学带来一个新的发现和创新时代。
