NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanics

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• dc.contributor.author Galvelis, Raimondas
• dc.contributor.author Varela-Rial, Alejandro
• dc.contributor.author Doerr, Stefan, 1987-
• dc.contributor.author Fino, Roberto
• dc.contributor.author Eastman, Peter
• dc.contributor.author Markland, Thomas E.
• dc.contributor.author Chodera, John D.
• dc.contributor.author De Fabritiis, Gianni
• dc.date.accessioned 2024-04-02T15:58:35Z
• dc.date.issued 2023
• dc.description.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.
• dc.description.sponsorship The authors thank the volunteers of GPUGRID.net for donating computing time. This project has received funding from the Torres-Quevedo Programme from the Spanish National Agency for Research (No. PTQ-17-09078/AEI/10.13039/501100011033) (R.G.); the European Union’s Horizon 2020 research and innovation programme, under Grant Agreement No. 823712 (R.G., A.V.-R., R.F.); the Industrial Doctorates Plan of the Secretariat of Universities and Research of the Department of Economy and Knowledge of the Generalitat of Catalonia (A.V.-R.); the Chan Zuckerberg Initiative DAF (Grant No. 2020-219414), an advised fund of Silicon Valley Community Foundation (S.D., P.E.); and the project PID2020-116564GB-I00 has been funded by MCIN/AEI/10.13039/501100011033. Research reported in this publication was supported by the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health, under Award No. GM140090 (P.E., T.E.M., J.D.C., G.D.F.). This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748 (J.D.C.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
• dc.format.mimetype application/pdf
• dc.identifier.citation Galvelis R, Varela-Rial A, Doerr S, Fino R, Eastman P, Markland TE, et al. NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanics. J Chem Inf Model. 2023 Sep 25;63(18):5701-8. DOI: 10.1021/acs.jcim.3c00773
• dc.identifier.doi http://dx.doi.org/10.1021/acs.jcim.3c00773
• dc.identifier.issn 1549-9596
• dc.identifier.uri http://hdl.handle.net/10230/59624
• dc.language.iso eng
• dc.publisher American Chemical Society (ACS)
• dc.relation.ispartof J Chem Inf Model. 2023 Sep 25;63(18):5701-8
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/823712
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-116564GB-I00
• dc.rights This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of chemical information and modeling, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://dx.doi.org/10.1021/acs.jcim.3c00773.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Chemical calculations
• dc.subject.keyword Computer simulations
• dc.subject.keyword Ligands
• dc.subject.keyword Molecular dynamics simulations
• dc.subject.keyword Molecules
• dc.title NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanics
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion