AMARO: All heavy-atom transferable neural network potentials of protein thermodynamics
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- dc.contributor.author Mirarchi, Antonio
- dc.contributor.author Peláez, Raúl P.
- dc.contributor.author Simeon, Guillem
- dc.contributor.author De Fabritiis, Gianni
- dc.date.accessioned 2025-01-14T13:56:33Z
- dc.date.available 2025-01-14T13:56:33Z
- dc.date.issued 2024
- dc.description.abstract All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.
- dc.description.sponsorship AM is financially supported by Generalitat de Catalunya’s Agency for Management of University and Research Grants (AGAUR) PhD grant 2024 FI-1-00278; the project PID2023-151620OB-I00 has been funded by MCIN/AEI/10.13039/501100011033. Research reported in this publication was partially supported by the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health under award number R01GM140090. 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 Mirarchi A, Peláez RP, Simeon G, De Fabritiis G. AMARO: All heavy-atom transferable neural network potentials of protein thermodynamics. J Chem Theory Comput. 2024 Nov 26;20(22):9871-8. DOI: 10.1021/acs.jctc.4c01239
- dc.identifier.doi http://dx.doi.org/10.1021/acs.jctc.4c01239
- dc.identifier.issn 1549-9618
- dc.identifier.uri http://hdl.handle.net/10230/69120
- dc.language.iso eng
- dc.publisher American Chemical Society (ACS)
- dc.relation.ispartof J Chem Theory Comput. 2024 Nov 26;20(22):9871-8
- dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2023-151620OB-I00
- dc.rights Copyright © 2024 American Chemical Society. This publication is licensed under CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Conformation
- dc.subject.keyword Energy
- dc.subject.keyword Free energy
- dc.subject.keyword Hydrogen
- dc.subject.keyword Molecular dynamics simulations
- dc.title AMARO: All heavy-atom transferable neural network potentials of protein thermodynamics
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion