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

Baczewska, Paulina; Kulczykowski, Michał; Zambroń, Bartosz; Jaszczewska-Adamczak, Joanna; Pakulski, Zbigniew; Roszak, Rafał; Grzybowski, Bartosz A.; Młynarski, Jacek

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

Abstract

Organic-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.

Citation

Angew. Chem. Int. Ed. 2024, e202318487 ; https://doi.org/10.1002/anie.202318487

URI

https://open.icm.edu.pl/handle/123456789/24717

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