ArticleThe journal of physical chemistry. B2019
Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.
Article in The journal of physical chemistry. B, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development.ACS omega · 2024Review
- Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.The Journal of chemical physics · 2023Article
- Modeling of HScientific reports · 2023Article
- Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways.ACS physical chemistry Au · 2022Article
- Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction.Chemical science · 2022Article
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.Chemical reviews · 2021Article
- Machine Learning Force Fields.Chemical reviews · 2021Article
- Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications.Journal of computer-aided molecular design · 2021Article
- Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach.Communications chemistry · 2020Article
- Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.Frontiers in chemistry · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
For exploration of chemical and biological systems, the combined quantum mechanics and molecular mechanics (QM/MM) and machine learning (ML) models have been developed recently to achieve high accuracy and efficiency for molecular dynamics (MD) simulations. Despite its success on reaction free energy calculations, how to identify new configurations on insufficiently sampled regions during MD and how to update the current ML models with the growing database on the fly are both very important but still challenging. In this article, we apply the QM/MM ML method to solvation free energy calculations and address these two challenges. We employ three approaches to detect new data points and introduce the gradient boosting algorithm to reoptimize efficiently the ML model during ML-based MD sampling. The solvation free energy calculations on several typical organic molecules demonstrate that our developed method provides a systematic, robust, and efficient way to explore new chemistry using ML-based QM/MM MD simulations.
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Registered trials
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