Sabanés Zariquiey, FrancescGalvelis, RaimondasGallicchio, EmilioChodera, John D.Markland, Thomas E.De Fabritiis, Gianni2024-04-022024Sabanés 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.3c020311549-9596http://hdl.handle.net/10230/59626This 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.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.3c02031.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1021/acs.jcim.3c02031Chemical calculationsFree energyLigandsMolecular mechanicsPeptidesProteinsinfo:eu-repo/semantics/embargoedAccess