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

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...

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Published in:Angewandte Chemie International Edition 2024-09, Vol.63 (37), p.e202318487-n/a
Main Authors: Baczewska, Paulina, Kulczykowski, Michał, Zambroń, Bartosz, Jaszczewska‐Adamczak, Joanna, Pakulski, Zbigniew, Roszak, Rafał, Grzybowski, Bartosz A., Mlynarski, Jacek
Format: Article
Language:English
Subjects:
Algorithms
Asymmetric catalysis
Asymmetry
Catalysis
Catalysts
Chemical reactions
Learning algorithms
Machine Learning
Magnesium
Neural networks
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Summary: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‐the‐box predictions successfully validated by experiment, e.g., in synthetically demanding asymmetric reductions or Michael additions. A carefully curated dataset of reactions catalyzed by magnesium‐based catalysts underlies a Machine‐Learning model guiding the choices of catalysts “optimal” for reactions unseen during training. When this model is tested by experiment, it suggests catalysts that yield higher ee values, replace rare‐earth‐metal catalysts, or improve the efficiency of stereoselective transformations relevant to drug discovery.
ISSN:1433-7851
1521-3773
1521-3773
DOI:10.1002/anie.202318487