. 2024 Jan 11;128(1):109-116.

doi: 10.1021/acs.jpcb.3c06662. Epub 2023 Dec 28.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Peter Eastman¹, Raimondas Galvelis^{2

3}, Raúl P Peláez³, Charlles R A Abreu^{4

5}, Stephen E Farr⁶, Emilio Gallicchio^{7

8}, Anton Gorenko⁹, Michael M Henry¹⁰, Frank Hu¹, Jing Huang¹¹, Andreas Krämer¹², Julien Michel⁶, Joshua A Mitchell¹³, Vijay S Pande^{14

15}, João Pglm Rodrigues¹⁵, Jaime Rodriguez-Guerra¹⁶, Andrew C Simmonett¹⁷, Sukrit Singh¹⁰, Jason Swails¹⁸, Philip Turner¹⁹, Yuanqing Wang²⁰, Ivy Zhang^{10

21}, John D Chodera¹⁰, Gianni De Fabritiis^{2

3

22}, Thomas E Markland¹

Affiliations

¹ Department of Chemistry, Stanford University, Stanford, California 94305, United States.
² Acellera Laboratories, C Dr Trueta 183, 08005 Barcelona, Spain.
³ Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.
⁴ Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 68542, Brazil.
⁵ Redesign Science Inc., 180 Varick St., New York, New York 10014, United States.
⁶ EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh EH9 3FJ, United Kingdom.
⁷ Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210-2889, United States.
⁸ 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.
⁹ Stream HPC, Koningin Wilhelminaplein 1-40601, 1062 HG Amsterdam, Netherlands.
¹⁰ Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York New York 10065, United States.
¹¹ Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.
¹² Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany.
¹³ The Open Force Field Initiative, Open Molecular Software Foundation, Davis, California 95616, United States.
¹⁴ Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, California 94025, United States.
¹⁵ Department of Structural Biology, Stanford University, Stanford, California 94305, United States.
¹⁶ Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117 Berlin, Germany.
¹⁷ Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States.
¹⁸ Entos Inc., 9310 Athena Circle, La Jolla, California 92037, United States.
¹⁹ College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.
²⁰ Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24 Waverly Place, New York, New York 10004, United States.
²¹ Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, New York 10065, United States.
²² ICREA, Passeig Lluis Companys 23, 08010 Barcelona, Spain.

PMID: 38154096
PMCID: PMC10846090
DOI: 10.1021/acs.jpcb.3c06662

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Peter Eastman et al. J Phys Chem B. 2024.

. 2024 Jan 11;128(1):109-116.

doi: 10.1021/acs.jpcb.3c06662. Epub 2023 Dec 28.

Authors

Affiliations

¹ Department of Chemistry, Stanford University, Stanford, California 94305, United States.
² Acellera Laboratories, C Dr Trueta 183, 08005 Barcelona, Spain.
³ Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.
⁴ Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 68542, Brazil.
⁵ Redesign Science Inc., 180 Varick St., New York, New York 10014, United States.
⁶ EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh EH9 3FJ, United Kingdom.
⁷ Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210-2889, United States.
⁸ 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.
⁹ Stream HPC, Koningin Wilhelminaplein 1-40601, 1062 HG Amsterdam, Netherlands.
¹⁰ Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York New York 10065, United States.
¹¹ Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.
¹² Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany.
¹³ The Open Force Field Initiative, Open Molecular Software Foundation, Davis, California 95616, United States.
¹⁴ Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, California 94025, United States.
¹⁵ Department of Structural Biology, Stanford University, Stanford, California 94305, United States.
¹⁶ Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117 Berlin, Germany.
¹⁷ Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States.
¹⁸ Entos Inc., 9310 Athena Circle, La Jolla, California 92037, United States.
¹⁹ College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.
²⁰ Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24 Waverly Place, New York, New York 10004, United States.
²¹ Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, New York 10065, United States.
²² ICREA, Passeig Lluis Companys 23, 08010 Barcelona, Spain.

PMID: 38154096
PMCID: PMC10846090
DOI: 10.1021/acs.jpcb.3c06662

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.

PubMed Disclaimer

Figures

**Figure 1:**
The 53 atom inhibitor bound to CDK8.

**Figure 2:**
Anionic GFP chromophore in water. The dihedral ɸ_P is defined between the C_P atom and the carbons of the methine bridge and the dihedral ɸ_I is defined between the N_I atom and the carbons of the methine bridge.

**Figure 3:**
Equivariant transformer training and validation loss.

**Figure 4:**
Anionic oxygen-oxygen RDF (left) and carbonyl oxygen-oxygen RDF (right) generated from the MLP and AIMD trajectories. The shaded region represents +/− 1 standard deviation across the 10 AIMD trajectories.

**Figure 5:**
Distribution of dihedral angles for ɸ_I and ɸ_P. The shaded region represents +/− 1 standard deviation across the 10 AIMD trajectories.

See this image and copyright information in PMC

Update of

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.
Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, Gorenko A, Henry MM, Hu F, Huang J, Krämer A, Michel J, Mitchell JA, Pande VS, Rodrigues JP, Rodriguez-Guerra J, Simmonett AC, Singh S, Swails J, Turner P, Wang Y, Zhang I, Chodera JD, Fabritiis G, Markland TE. Eastman P, et al. ArXiv [Preprint]. 2023 Nov 29:arXiv:2310.03121v2. ArXiv. 2023. Update in: J Phys Chem B. 2024 Jan 11;128(1):109-116. doi: 10.1021/acs.jpcb.3c06662. PMID: 37986730 Free PMC article. Updated. Preprint.

References

1. Eastman P; Swails J; Chodera JD; McGibbon RT; Zhao Y; Beauchamp KA; Wang L-P; Simmonett AC; Harrigan MP; Stern CD; et al. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLOS Comput. Biol 2017, 13 (7), e1005659. 10.1371/journal.pcbi.1005659. - DOI - PMC - PubMed
1. Smith JS; Isayev O; Roitberg AE ANI-1: An Extensible Neural Network Potential with DFT Accuracy at Force Field Computational Cost. Chem. Sci 2017, 8 (4), 3192–3203. 10.1039/C6SC05720A. - DOI - PMC - PubMed
1. Batzner S; Musaelian A; Sun L; Geiger M; Mailoa JP; Kornbluth M; Molinari N; Smidt TE; Kozinsky B E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. Nat. Commun 2022, 13 (1), 2453. 10.1038/s41467-022-29939-5. - DOI - PMC - PubMed
1. Thölke P; De Fabritiis G TorchMD-NET: Equivariant Transformers for Neural Network Based Molecular Potentials. arXiv April 23, 2022. 10.48550/arXiv.2202.02541. - DOI
1. Musaelian A; Batzner S; Johansson A; Sun L; Owen CJ; Kornbluth M; Kozinsky B Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics. Nat. Commun 2023, 14 (1), 579. 10.1038/s41467-023-36329-y. - DOI - PMC - PubMed

Publication types

Actions
Actions

MeSH terms

Actions
Actions
Actions

Substances

Actions

Grants and funding

LinkOut - more resources

Full Text Sources
Other Literature Sources
- The Lens - Patent Citations Database

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Affiliations

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Authors

Affiliations

Abstract

Figures

Update of

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources