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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

  • Peter Eastman*
    Peter Eastman
    Department of Chemistry, Stanford University, Stanford, California 94305, United States
    *Email: [email protected]
  • Raimondas Galvelis
    Raimondas Galvelis
    Acellera Laboratories, C Dr Trueta 183, 08005 Barcelona, Spain
    Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
  • Raúl P. Peláez
    Raúl P. Peláez
    Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
  • Charlles R. A. Abreu
    Charlles R. A. Abreu
    Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 68542, Brazil
    Redesign Science Inc., 180 Varick St., New York, New York 10014, United States
  • Stephen E. Farr
    Stephen E. Farr
    EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh EH9 3FJ, United Kingdom
  • Emilio Gallicchio
    Emilio Gallicchio
    Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210-2889, United States
    Ph.D. Program in Chemistry and Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
  • Anton Gorenko
    Anton Gorenko
    Stream HPC, Koningin Wilhelminaplein 1−40601, 1062 HG Amsterdam, Netherlands
  • Michael M. Henry
    Michael M. Henry
    Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York New York 10065, United States
  • Frank Hu
    Frank Hu
    Department of Chemistry, Stanford University, Stanford, California 94305, United States
    More by Frank Hu
  • Jing Huang
    Jing Huang
    Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
    More by Jing Huang
  • Andreas Krämer
    Andreas Krämer
    Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany
  • Julien Michel
    Julien Michel
    EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh EH9 3FJ, United Kingdom
  • Joshua A. Mitchell
    Joshua A. Mitchell
    The Open Force Field Initiative, Open Molecular Software Foundation, Davis, California 95616, United States
  • Vijay S. Pande
    Vijay S. Pande
    Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, California 94025, United States
    Department of Structural Biology, Stanford University, Stanford, California 94305, United States
  • João PGLM Rodrigues
    João PGLM Rodrigues
    Department of Structural Biology, Stanford University, Stanford, California 94305, United States
  • Jaime Rodriguez-Guerra
    Jaime Rodriguez-Guerra
    Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117 Berlin, Germany
  • Andrew C. Simmonett
    Andrew C. Simmonett
    Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
  • Sukrit Singh
    Sukrit Singh
    Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York New York 10065, United States
    More by Sukrit Singh
  • Jason Swails
    Jason Swails
    Entos Inc., 9310 Athena Circle, La Jolla, California 92037, United States
    More by Jason Swails
  • Philip Turner
    Philip Turner
    College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
  • Yuanqing Wang
    Yuanqing Wang
    Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24 Waverly Place, New York, New York 10004, United States
  • Ivy Zhang
    Ivy Zhang
    Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York New York 10065, United States
    Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, New York 10065, United States
    More by Ivy Zhang
  • John D. Chodera
    John D. Chodera
    Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York New York 10065, United States
  • Gianni De Fabritiis
    Gianni De Fabritiis
    Acellera Laboratories, C Dr Trueta 183, 08005 Barcelona, Spain
    Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
    ICREA, Passeig Lluis Companys 23, 08010 Barcelona, Spain
  • , and 
  • Thomas E. Markland
    Thomas E. Markland
    Department of Chemistry, Stanford University, Stanford, California 94305, United States
Cite this: J. Phys. Chem. B 2024, 128, 1, 109–116
Publication Date (Web):December 28, 2023
https://doi.org/10.1021/acs.jpcb.3c06662
Copyright © 2023 American Chemical Society

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    Abstract

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    Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.

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    Cited By

    This article is cited by 3 publications.

    1. Kirill Zinovjev, Lester Hedges, Rubén Montagud Andreu, Christopher Woods, Iñaki Tuñón, Marc W. van der Kamp. emle-engine: A Flexible Electrostatic Machine Learning Embedding Package for Multiscale Molecular Dynamics Simulations. Journal of Chemical Theory and Computation 2024, 20 (11) , 4514-4522. https://doi.org/10.1021/acs.jctc.4c00248
    2. Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis. TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations. Journal of Chemical Theory and Computation 2024, 20 (10) , 4076-4087. https://doi.org/10.1021/acs.jctc.4c00253
    3. Sara Tkaczyk, Johannes Karwounopoulos, Andreas Schöller, H. Lee Woodcock, Thierry Langer, Stefan Boresch, Marcus Wieder. Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential. Journal of Chemical Theory and Computation 2024, 20 (7) , 2719-2728. https://doi.org/10.1021/acs.jctc.3c01274