ArticleJournal of the American Chemical Society2026
Predicting Enantioselectivity of Ruthenium-Catalyzed Ketone Hydrogenation with 3D Structure-Based Deep Learning.
Nicholas Hadler, Evelyn Ramirez, Matthew Avaylon, Masha Elkin, N Ian Rinehart, Michael W Mahoney, Gunther H Weber, Joscha Hoche, Jan Gerit Brandenburg, Talita Perciano and 1 more
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In one paragraphArticle in Journal of the American Chemical Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 · Who and what moneyAuthors and funding
11 authors.
Nicholas HadlerDepartment of Chemistry, University of California, Berkeley, Berkeley, California94720, United States.ORCID 0000-0002-9167-5227 Evelyn RamirezDepartment of Chemistry, University of California, Berkeley, Berkeley, California94720, United States.
Matthew AvaylonScientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States.
Masha ElkinDepartment of Chemistry, University of California, Berkeley, Berkeley, California94720, United States.ORCID 0000-0002-4979-6420 N Ian RinehartDepartment of Chemistry, University of California, Berkeley, Berkeley, California94720, United States.ORCID 0000-0002-3106-5208 Michael W MahoneyScientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States.
Gunther H WeberScientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States.
Joscha HocheMerck KGaA, Frankfurter Strasse 250, Darmstadt, 64293, Germany.
Jan Gerit BrandenburgMerck KGaA, Frankfurter Strasse 250, Darmstadt, 64293, Germany.
Talita PercianoScientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, California94720, United States.
John F HartwigDepartment of Chemistry, University of California, Berkeley, Berkeley, California94720, United States.ORCID 0000-0002-4157-468X Funding
Discovery and Development of Organic Reactions Catalyzed by Transition Metals Valuable for Medicinal ChemistryR35GM130387 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI John F Hartwig · 2019 to 2026
$7.1MGPU Computing Cluster for Transformative Public Health driven Molecular ScienceS10OD034382 · OD · UNIVERSITY OF CALIFORNIA BERKELEY · PI DURKIN, KATHLEEN A · 2023 to 2023
$491kAcquisition of a Cryoprobe and Related Accessories for a 600 MHz NMR Spectrometer to Support Health Research Activities at the University of California, BerkeleyS10OD024998 · OD · UNIVERSITY OF CALIFORNIA BERKELEY · PI HARTWIG, JOHN F · 2018 to 2018
$265kFormation of C-C Bonds from Unactivated C(sp3)-H Bonds of Hydrosilanes Derived from Common Functional GroupsF32GM134579 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI ELKIN, MASHA · 2019 to 2022
$194kNational Institutes of Health (NIH) 1R35GM130387National Institutes of Health (NIH) F32-GM134579National Institutes of Health (NIH) S10OD024998National Institutes of Health (NIH) S10OD034382National Science Foundation (NSF) DGE 2146752NIGMS NIH HHS F32 GM134579NIGMS NIH HHS R35 GM130387NIH HHS S10 OD024998NIH HHS S10 OD034382U.S. Department of Energy (DOE) DE-AC02-05CH11231
6 · The paper itselfAbstract
We report machine-learning (ML) models that predict with high accuracy the enantioselectivity of ketone hydrogenation with Noyori-type diphosphine-diamine catalysts. To mitigate bias in published data, we combined a data set curated from the literature with >1,000 examples from our own high-throughput experimentation. We demonstrate that 3D graph neural networks, trained on 3D representations derived from the enantiodetermining transition state, outperform models based on 2D graphs, molecular fingerprints, and descriptors derived from semiempirical quantum calculations. These results indicate that models trained on 3D structural representations that reflect catalyst-substrate interactions relevant to enantioselectivity can accurately extrapolate to structures distinct from those in the training set.
Identifiers
PMID42490528
PMCPMC13485326
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