Machine Learning Algorithm Guides Catalyst Choices for Magnesium-Catalyzed Asymmetric Reactions

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dc.contributor.authorBaczewska, Paulina
dc.contributor.authorKulczykowski, Michał
dc.contributor.authorZambroń, Bartosz
dc.contributor.authorJaszczewska-Adamczak, Joanna
dc.contributor.authorPakulski, Zbigniew
dc.contributor.authorRoszak, Rafał
dc.contributor.authorGrzybowski, Bartosz A.
dc.contributor.authorMłynarski, Jacek
dc.contributor.organizationInstitute of Organic Chemistry, Polish Academy of Sciences
dc.contributor.organizationUlsan National Institute of Science and Technology, South Korea
dc.date.accessioned2024-08-20T15:14:02Z
dc.date.available2024-08-20T15:14:02Z
dc.date.issued2024
dc.description.abstractOrganic-chemical literature encompasses large numbers of catalysts and reactions they can effect. Many of these examples are published merely to document the catalysts’ scope but do not necessarily guarantee that a given catalyst is “optimal”—in terms of yield or enantiomeric excess—for a particular reaction. This paper describes a Machine Learning model that aims to improve such catalyst-reaction assignments based on the carefully curated literature data. As we show here for the case of asymmetric magnesium catalysis, this model achieves relatively high accuracy and offers out of-thebox predictions successfully validated by experiment, e.g., in synthetically demanding asymmetric reductions or Michael additions.en
dc.description.sponsorshipPolish National Science Centre (OPUS Grant NCN 2021/41/B/ST4/01617); Institute for Basic Science, Korea (project code IBS-R020-D1).
dc.identifier.citationAngew. Chem. Int. Ed. 2024, e202318487 ; https://doi.org/10.1002/anie.202318487
dc.identifier.doi10.1002/anie.202318487
dc.identifier.issn1433-7851
dc.identifier.issn1521-3773
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/24717
dc.language.isoen
dc.publisherWiley-VCH GmbH
dc.rightsUznanie autorstwa 4.0 Międzynarodoween
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceAngewandte Chemie International Edition
dc.titleMachine Learning Algorithm Guides Catalyst Choices for Magnesium-Catalyzed Asymmetric Reactionsen
dc.typearticle
dc.type.versionpublishedVersion
person.identifier.orcidZambroń, Bartosz [0000-0002-7170-4174]
person.identifier.orcidJaszczewska-Adamczak, Joanna [0000-0001-7407-8263]
person.identifier.orcidPakulski, Zbigniew [0000-0002-8085-0050]
person.identifier.orcidGrzybowski, Bartosz A. [0000-0001-6613-4261]
person.identifier.orcidMłynarski, Jacek [0000-0002-1794-306X]
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