MLIMC: Machine learning-based implicit-solvent Monte Carlo
This work addresses computational efficiency for molecular simulations in chemistry and biology, though it appears incremental as it builds on existing implicit-solvent models.
The authors tackled the high computational cost of explicit-solvent Monte Carlo methods by developing a machine learning-based implicit-solvent Monte Carlo (MLIMC) method, which combines the accuracy of Poisson-Boltzmann models with the efficiency of Generalized Born models, showing great advantages in speed and accuracy for molecular structure optimization and prediction in benzene-water and protein-water systems.
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.