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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.
Eastman, Peter; Galvelis, Raimondas; Peláez, Raúl P; Abreu, Charlles R A; Farr, Stephen E; Gallicchio, Emilio; Gorenko, Anton; Henry, Michael M; Hu, Frank; Huang, Jing; Krämer, Andreas; Michel, Julien; Mitchell, Joshua A; Pande, Vijay S; Rodrigues, João Pglm; Rodriguez-Guerra, Jaime; Simmonett, Andrew C; Singh, Sukrit; Swails, Jason; Turner, Philip; Wang, Yuanqing; Zhang, Ivy; Chodera, John D; De Fabritiis, Gianni; Markland, Thomas E.
Afiliación
  • Eastman P; Department of Chemistry, Stanford University, Stanford, California 94305, United States.
  • Galvelis R; Acellera Laboratories, C Dr Trueta 183, 08005 Barcelona, Spain.
  • Peláez RP; Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.
  • Abreu CRA; Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.
  • Farr SE; Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 68542, Brazil.
  • Gallicchio E; Redesign Science Inc., 180 Varick St., New York, New York 10014, United States.
  • Gorenko A; EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh EH9 3FJ, United Kingdom.
  • Henry MM; Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210-2889, United States.
  • Hu F; 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.
  • Huang J; Stream HPC, Koningin Wilhelminaplein 1-40601, 1062 HG Amsterdam, Netherlands.
  • Krämer A; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York New York 10065, United States.
  • Michel J; Department of Chemistry, Stanford University, Stanford, California 94305, United States.
  • Mitchell JA; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.
  • Pande VS; Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany.
  • Rodrigues JP; EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh EH9 3FJ, United Kingdom.
  • Rodriguez-Guerra J; The Open Force Field Initiative, Open Molecular Software Foundation, Davis, California 95616, United States.
  • Simmonett AC; Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, California 94025, United States.
  • Singh S; Department of Structural Biology, Stanford University, Stanford, California 94305, United States.
  • Swails J; Department of Structural Biology, Stanford University, Stanford, California 94305, United States.
  • Turner P; Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117 Berlin, Germany.
  • Wang Y; Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States.
  • Zhang I; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York New York 10065, United States.
  • Chodera JD; Entos Inc., 9310 Athena Circle, La Jolla, California 92037, United States.
  • De Fabritiis G; College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.
  • Markland TE; Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24 Waverly Place, New York, New York 10004, United States.
J Phys Chem B ; 128(1): 109-116, 2024 01 11.
Article en En | MEDLINE | ID: mdl-38154096
ABSTRACT
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Agua / Simulación de Dinámica Molecular Idioma: En Revista: J Phys Chem B Asunto de la revista: QUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Agua / Simulación de Dinámica Molecular Idioma: En Revista: J Phys Chem B Asunto de la revista: QUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos
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