MLIMCL：基于机器学习的隐式溶剂蒙特卡罗方法

对于分子结构的优化和预测，蒙特卡罗(MC)是很重要的计算工具. 当溶剂效应被显式的考虑时，由于水分子和电离子的自由度很大，蒙特卡罗方法变得非常昂贵. 相对而言，基于隐式溶剂的蒙特卡罗方法可以通过对溶剂效应平均场的近似来大大降低计算成本，同时还能保持目标分子在原子水平上的细节. 目前两种最流行的隐式溶剂模型是泊松-波兹曼模型和通用化波恩模型. 通用化波恩模型是泊松-波兹曼模型的近似，但在模拟计算时间上要快得多. 本文通过结合两种隐式溶剂模型在准确性和效率方面的优势，开发了一种基于机器学习的隐式溶剂蒙特卡罗方法. 具体而言，蒙特卡罗方法通过机器学习既保留了泊松-波兹曼模型的精度，又达到了通用化波恩模型的速度，从而能快速准确地获取模拟计算中每一步的静电溶解自由能. 本文采用苯-水系统和蛋白质-水系统来验证我们的蒙特卡罗方法. 实验证明蒙特卡罗方法在分子结构优化和预测的速度和准确性方面具有很大优势.

MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo

Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.