ArticleChinese journal of chemical physics2021
MLIMC: Machine Learning-Based Implicit-Solvent Monte Carlo.
Article in Chinese journal of chemical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A DNN Biophysics Model with Topological and Electrostatic Features.Journal of chemical information and modeling · 2026Article
- Calculation of solvation force in molecular dynamics simulation by deep-learning method.Biophysical journal · 2024Article
- Bridging Eulerian and Lagrangian Poisson-Boltzmann solvers by ESES.Journal of computational chemistry · 2024Article
- Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models.Briefings in bioinformatics · 2023Review
- Article
- Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pJournal of chemical theory and computation · 2022Article
- Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2.Chemical reviews · 2022Review
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Authors and funding
3 authors.
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Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.