Enhancing protein-ligand binding affinity predictions using neural network potentials

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• dc.contributor.author Sabanés Zariquiey, Francesc
• dc.contributor.author Galvelis, Raimondas
• dc.contributor.author Gallicchio, Emilio
• dc.contributor.author Chodera, John D.
• dc.contributor.author Markland, Thomas E.
• dc.contributor.author De Fabritiis, Gianni
• dc.date.accessioned 2024-04-02T15:58:41Z
• dc.date.embargoEnd info:eu-repo/date/embargoEnd/2025-02-20
• dc.date.issued 2024
• dc.description.abstract This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.
• dc.description.sponsorship The authors thank the volunteers of GPUGRID.net for donating computing time. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 823712, and the project PID2020-116564GB-I00 has been funded by MCIN/AEI/10.13039/501100011033; the Torres-Quevedo Programme from the Spanish National Agency for Research (PTQ2020-011145/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 Number R01GM140090 to T.E.M., G.D.F., and J.D.C. E.G. acknowledges support from the United States National Science Foundation (NSF CAREER 1750511). J.D.C. also acknowledges support from NIH Grant P30CA008748 and the Sloan Kettering Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
• dc.embargo.liftdate 2025-02-20
• dc.format.mimetype application/pdf
• dc.identifier.citation Sabanés Zariquiey F, Galvelis R, Gallicchio E, Chodera JD, Markland TE, De Fabritiis G. Enhancing protein-ligand binding affinity predictions using neural network potentials. J Chem Inf Model. 2024 Mar 11;64(5):1481-5. DOI: 10.1021/acs.jcim.3c02031
• dc.identifier.doi http://dx.doi.org/10.1021/acs.jcim.3c02031
• dc.identifier.issn 1549-9596
• dc.identifier.uri http://hdl.handle.net/10230/59626
• dc.language.iso eng
• dc.publisher American Chemical Society (ACS)
• dc.relation.ispartof J Chem Inf Model. 2024 Mar 11;64(5):1481-5
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/823712
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-116564GB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PTQ2020-011145
• 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.3c02031.
• dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
• dc.subject.keyword Chemical calculations
• dc.subject.keyword Free energy
• dc.subject.keyword Ligands
• dc.subject.keyword Molecular mechanics
• dc.subject.keyword Peptides
• dc.subject.keyword Proteins
• dc.title Enhancing protein-ligand binding affinity predictions using neural network potentials
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion