{"doi":"10.1039/d4sc00690a","title":"Machine-learned molecular mechanics force fields from large-scale quantum chemical data","abstract":"The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.","journal":"Chemical Science","year":2024,"id":419493,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9494,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":906224,"name":"Anika J. Friedman","orcid":"0000-0002-5427-2779","position":1,"is_corresponding":false},{"id":467931,"name":"Chapin E. Cavender","orcid":"0000-0002-5899-7953","position":2,"is_corresponding":false},{"id":859465,"name":"Pavan Kumar Behara","orcid":"0000-0001-6583-2148","position":3,"is_corresponding":false},{"id":851161,"name":"Iván Pulido","orcid":"0000-0002-7178-8136","position":4,"is_corresponding":false},{"id":851162,"name":"Michael M. Henry","orcid":"0000-0002-3870-9993","position":5,"is_corresponding":false},{"id":687782,"name":"Hugo MacDermott-Opeskin","orcid":"0000-0002-7393-7457","position":6,"is_corresponding":false},{"id":1208979,"name":"Christopher R. Iacovella","orcid":"0000-0003-0557-0427","position":7,"is_corresponding":false},{"id":1208980,"name":"Arnav Nagle","orcid":"0009-0002-6749-4917","position":8,"is_corresponding":false},{"id":559513,"name":"Alexander Matthew Payne","orcid":"0000-0003-0947-0191","position":9,"is_corresponding":false},{"id":56175,"name":"Michael R. Shirts","orcid":"0000-0003-3249-1097","position":10,"is_corresponding":false},{"id":263939,"name":"David L. Mobley","orcid":"0000-0002-1083-5533","position":11,"is_corresponding":false},{"id":263940,"name":"John D. Chodera","orcid":"0000-0003-0542-119X","position":12,"is_corresponding":false},{"id":851159,"name":"Yuanqing Wang","orcid":"0000-0003-4403-2015","position":13,"is_corresponding":false},{"id":851163,"name":"Kenichiro Takaba","orcid":"0000-0002-2481-8830","position":0,"is_corresponding":true}],"reference_count":146,"raw_metadata":null,"created_at":"2026-07-19T01:57:14.586331Z","pmid":"39148808","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}