ArticleJournal of chemical information and modeling2023
NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.Journal of chemical theory and computation · 2026Article
- Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.Journal of the American Chemical Society · 2026Article
- Adaptive Normal Mode Sampling (aMDeNM) Enhances Exploration of Protein Conformational Space and Reveals the Functional Role of Frequency Coupling.Journal of chemical theory and computation · 2026Article
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- Simulating enzyme catalysis with electrostatically embedded machine learning potentials.Chemical science · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.ArXiv · 2026Article
- CHARMM-GUI Hybrid ML/MM Builder for Hybrid Machine Learning and Molecular Mechanical Modeling and Simulations.Journal of chemical information and modeling · 2026Article
- Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- LamNet: an alchemical-path-aware graph neural network to accelerate binding free energy calculations for drug discovery and beyond.National science review · 2026Article
- Efficient and accurate spatial mixing of machine learned interatomic potentials for materials science.npj computational materials · 2026Article
- Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.Journal of chemical information and modeling · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Integrating Machine Learning into Free Energy Perturbation Workflows.Journal of chemical information and modeling · 2025Review
- Mechanistic Insights into SAM-Dependent Methyltransferases: A Review of Computational Approaches.International journal of molecular sciences · 2025Review
- Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.Journal of chemical theory and computation · 2025Article
- A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties.Chemical reviews · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared with traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines a neural network potential (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by ∼5 times and achieve a combined sampling of 1 μs for each complex, marking the longest simulations ever reported for this class of 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.