ArticleJournal of chemical theory and computation2016
Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks.
Article in Journal of chemical theory and computation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 143 citations in OpenAlex.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- Minimization of Disorder as a Key Design Principle for Natural Sizes of Light Harvesting 2 Complexes.The journal of physical chemistry letters · 2026Article
- Quantum Mechanical/Molecular Mechanical (QM/MM) Studies of Enzymes.Advances in experimental medicine and biology · 2026Review
- Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.The journal of physical chemistry. B · 2025Article
- Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions.Biophysics reviews · 2025Review
- Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.Journal of chemical theory and computation · 2025Article
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
- Article
- Modelling chemical processes in explicit solvents with machine learning potentials.Nature communications · 2024Article
- Nanoscale chemical reaction exploration with a quantum magnifying glass.Nature communications · 2024Article
- Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine-Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials.Molecules (Basel, Switzerland) · 2024Article
- Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.Nature communications · 2024Article
- Training machine learning potentials for reactive systems: A Colab tutorial on basic models.Journal of computational chemistry · 2024Article
- Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development.ACS omega · 2024Review
- Free energies at QM accuracy from force fields via multimap targeted estimation.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling.Journal of chemical theory and computation · 2023Article
- Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation.The Journal of chemical physics · 2023Article
- Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.The Journal of chemical physics · 2023Article
- Machine Learning Exciton Hamiltonians in Light-Harvesting Complexes.Journal of chemical theory and computation · 2023Article
- Protein dynamics: The future is bright and complicated!Structural dynamics (Melville, N.Y.) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Molecular dynamics simulation with multiscale quantum mechanics/molecular mechanics (QM/MM) methods is a very powerful tool for understanding the mechanism of chemical and biological processes in solution or enzymes. However, its computational cost can be too high for many biochemical systems because of the large number of ab initio QM calculations. Semiempirical QM/MM simulations have much higher efficiency. Its accuracy can be improved with a correction to reach the ab initio QM/MM level. The computational cost on the ab initio calculation for the correction determines the efficiency. In this paper we developed a neural network method for QM/MM calculation as an extension of the neural-network representation reported by Behler and Parrinello. With this approach, the potential energy of any configuration along the reaction path for a given QM/MM system can be predicted at the ab initio QM/MM level based on the semiempirical QM/MM simulations. We further applied this method to three reactions in water to calculate the free energy changes. The free-energy profile obtained from the semiempirical QM/MM simulation is corrected to the ab initio QM/MM level with the potential energies predicted with the constructed neural network. The results are in excellent accordance with the reference data that are obtained from the ab initio QM/MM molecular dynamics simulation or corrected with direct ab initio QM/MM potential energies. Compared with the correction using direct ab initio QM/MM potential energies, our method shows a speed-up of 1 or 2 orders of magnitude. It demonstrates that the neural network method combined with the semiempirical QM/MM calculation can be an efficient and reliable strategy for chemical reaction simulations.
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Registered trials
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.