Wang Yibo, Zhang Cong, Tang Ke, Wang Xiaohui
Laboratory of Chemical Biology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China.
State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing 100191, China.
Fundam Res. 2022 Jun 12;5(4):1478-1480. doi: 10.1016/j.fmre.2022.06.002. eCollection 2025 Jul.
Creating an in silico all-atom whole-cell model for molecular dynamics (MD) simulation is one of the best ways to quantitatively understand the basic structure and function of cells in terms of the laws of physics and chemistry. The heavy use of graphics processing units (GPUs), the exponential growth of supercomputing power, and the emergence of MD simulation-specific supercomputers lay the groundwork for the MD simulation of molecular machinery. Moreover, the involvement of artificial intelligence (AI) will not only improve the accuracy of the simulation but also significantly accelerate the sampling efficiency. However, several underlying critical puzzles prevent in silico all-atom whole-cell modeling, which is the holy grail of MD simulation. From this perspective, we briefly reviewed the accomplishments of present techniques and hardware as well as provided insight to address the challenge of MD simulation of a living cell. With the rapid advancements in computational hardware, AI, and experimental cell biology, it would be possible to achieve this overarching goal.
创建用于分子动力学(MD)模拟的计算机全原子全细胞模型,是从物理和化学定律的角度定量理解细胞基本结构和功能的最佳方法之一。图形处理单元(GPU)的大量使用、超级计算能力的指数级增长以及MD模拟专用超级计算机的出现,为分子机器的MD模拟奠定了基础。此外,人工智能(AI)的参与不仅会提高模拟的准确性,还会显著加快采样效率。然而,有几个潜在的关键难题阻碍了计算机全原子全细胞建模,而这正是MD模拟的圣杯。从这个角度来看,我们简要回顾了当前技术和硬件所取得的成就,并就应对活细胞MD模拟的挑战提供了见解。随着计算硬件、人工智能和实验细胞生物学的迅速发展,实现这一总体目标将成为可能。