Galvelis, RaimondasVarela-Rial, AlejandroDoerr, Stefan, 1987-Fino, RobertoEastman, PeterMarkland, Thomas E.Chodera, John D.De Fabritiis, Gianni2024-04-022023Galvelis 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.3c007731549-9596http://hdl.handle.net/10230/59624Machine 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.application/pdfengThis 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.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1021/acs.jcim.3c00773Chemical calculationsComputer simulationsLigandsMolecular dynamics simulationsMoleculesinfo:eu-repo/semantics/openAccess