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.
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MLIMC: Machine learning-based implicit-solvent Monte Carlo †
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December 2021
Research Article|
December 01 2021
MLIMC: Machine learning-based implicit-solvent Monte Carlo †
Special Collection:
Virtual issue on Theoretical and Computational Chemistry (2021)
Jiahui Chen;
Jiahui Chen
a
Department of Mathematics, Michigan State University
, MI 48824, USA
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Weihua Geng;
Weihua Geng
*
b
Department of Mathematics, Southern Methodist University
, Dallas, TX 75275, USA
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Guo-Wei Wei
Guo-Wei Wei
*
a
Department of Mathematics, Michigan State University
, MI 48824, USA
c
Department of Biochemistry and Molecular Biology, Michigan State University
, MI 48824, USA
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†
Part of Special Issue “John Z.H. Zhang Festschrift for celebrating his 60th birthday”.
Chin. J. Chem. Phys. 34, 683–694 (2021)
Article history
Received:
September 02 2021
Accepted:
September 24 2021
Citation
Jiahui Chen, Weihua Geng, Guo-Wei Wei; MLIMC: Machine learning-based implicit-solvent Monte Carlo. Chin. J. Chem. Phys. 1 December 2021; 34 (6): 683–694. https://doi.org/10.1063/1674-0068/cjcp2109150
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