Liu Yikai, Ghosh Tushar K, Lin Guang, Chen Ming
Department of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47906, United States.
Department of Chemistry, Purdue University, West Lafayette, Indiana 47906, United States.
J Phys Chem Lett. 2024 Apr 11;15(14):3938-3945. doi: 10.1021/acs.jpclett.3c03515. Epub 2024 Apr 3.
Biased enhanced sampling methods that utilize collective variables (CVs) are powerful tools for sampling conformational ensembles. Due to their large intrinsic dimensions, efficiently generating conformational ensembles for complex systems requires enhanced sampling on high-dimensional free energy surfaces. While temperature-accelerated molecular dynamics (TAMD) can trivially adopt many CVs in a simulation, unbiasing the simulation to generate unbiased conformational ensembles requires accurate modeling of a high-dimensional CV probability distribution, which is challenging for traditional density estimation techniques. Here we propose an unbiasing method based on the score-based diffusion model, a deep generative learning method that excels in density estimation across complex data landscapes. We demonstrate that this unbiasing approach, tested on multiple TAMD simulations, significantly outperforms traditional unbiasing methods and can generate accurate unbiased conformational ensembles. With the proposed approach, TAMD can adopt CVs that focus on improving sampling efficiency and the proposed unbiasing method enables accurate evaluation of ensemble averages of important chemical features.
利用集体变量（CVs）的有偏增强采样方法是用于采样构象系综的强大工具。由于其固有的高维度特性，要为复杂系统高效生成构象系综，就需要在高维自由能表面上进行增强采样。虽然温度加速分子动力学（TAMD）在模拟中可以轻松采用多个CVs，但要使模拟无偏以生成无偏的构象系综，就需要对高维CV概率分布进行精确建模，这对传统密度估计技术来说具有挑战性。在此，我们提出一种基于得分扩散模型的无偏方法，这是一种深度生成学习方法，在跨复杂数据景观的密度估计方面表现出色。我们证明，这种在多个TAMD模拟上进行测试的无偏方法显著优于传统无偏方法，并且能够生成准确的无偏构象系综。通过所提出的方法，TAMD可以采用专注于提高采样效率的CVs，并且所提出的无偏方法能够准确评估重要化学特征的系综平均值。
