QuantumBind-RBFE: Accurate relative binding free energy calculations using neural network potentials

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Sabanés Zariquiey, Francesc
• dc.contributor.author Farr, Stephen E.
• dc.contributor.author Doerr, Stefan, 1987-
• dc.contributor.author De Fabritiis, Gianni
• dc.date.accessioned 2025-06-13T07:45:27Z
• dc.date.available 2025-06-13T07:45:27Z
• dc.date.issued 2025
• dc.description.abstract Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we validate relative binding free energy (RBFE) accuracy using neural network potentials (NNPs) for the ligands. We utilize a novel NNP model, AceFF 1.0, based on the TensorNet architecture for small molecules that broadens the applicability to diverse drug-like compounds, including all important chemical elements and supporting charged molecules. Using established benchmarks, we show overall improved accuracy and correlation in binding affinity predictions compared with GAFF2 for molecular mechanics and ANI2-x for NNPs. Slightly less accuracy but comparable correlations with OPLS4. We also show that we can run the NNP simulations at 2 fs time step, at least two times larger than previous NNP models, providing significant speed gains. The results show promise for further evolutions of free energy calculations using NNPs while demonstrating its practical use already with the current generation. The code and NNP model are publicly available for research use.
• dc.format.mimetype application/pdf
• dc.identifier.citation Sabanés Zariquiey F, Farr SE, Doerr S, De Fabritiis G. QuantumBind-RBFE: Accurate relative binding free energy calculations using neural network potentials. J Chem Inf Model. 2025 Apr 28;65(8):4081-9. DOI: 10.1021/acs.jcim.5c00033
• dc.identifier.doi http://dx.doi.org/10.1021/acs.jcim.5c00033
• dc.identifier.issn 1549-9596
• dc.identifier.uri http://hdl.handle.net/10230/70675
• dc.language.iso eng
• dc.publisher American Chemical Society (ACS)
• dc.relation.ispartof J Chem Inf Model. 2025 Apr 28;65(8):4081-9
• dc.rights 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 Chemical calculations
• dc.subject.keyword Drug discovery
• dc.subject.keyword Free energy
• dc.subject.keyword Ligands
• dc.subject.keyword Molecular mechanics
• dc.title QuantumBind-RBFE: Accurate relative binding free energy calculations using neural network potentials
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion