Estimation of multicomponent reactions’ yields from networks of mechanistic steps

This work describes estimation of yields of complex, multicomponent reactions (MCRs) based on the modeled networks of mechanistic steps spanning both the main reaction pathway as well as immediate and downstream side reactions. Because experimental values of the kinetic rate constants for individual mechanistic transforms are extremely sparse, these constants are approximated here using Mayr's nucleophilicity and electrophilicity parameters fine-tuned by correction terms grounded in linear free-energy relationships. With this formalism, the model trained on the mechanistic networks of only 20 - but mechanistically- and yield-diverse MCRs - transfers well to newly discovered MCRs that are based on markedly different mechanisms and types of individual mechanistic transforms. These results suggest that mechanistic-level approach to yield estimation may be a useful alternative to models that are derived from full-reaction data and lack information about yield-lowering side reactions.

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Publication Details

Journal
Nature Communications
Published
2024-11-27
DOI
https://doi.org/10.1038/s41467-024-54550-1
Citations
7
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.82

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article

Estimation of multicomponent reactions’ yields from networks of mechanistic steps

Agnieszka Wołos, Sara Szymkuć, Bartosz A. Grzybowski, Rafał Roszak
7 citations
Nature Communications
Machine Learning in Materials Science
0.82
article

Estimation of multicomponent reactions’ yields from networks of mechanistic steps

Agnieszka Wołos, Sara Szymkuć, Bartosz A. Grzybowski, Rafał Roszak
article en
7 citations

Abstract

This work describes estimation of yields of complex, multicomponent reactions (MCRs) based on the modeled networks of mechanistic steps spanning both the main reaction pathway as well as immediate and downstream side reactions. Because experimental values of the kinetic rate constants for individual mechanistic transforms are extremely sparse, these constants are approximated here using Mayr's nucleophilicity and electrophilicity parameters fine-tuned by correction terms grounded in linear free-energy relationships. With this formalism, the model trained on the mechanistic networks of only 20 - but mechanistically- and yield-diverse MCRs - transfers well to newly discovered MCRs that are based on markedly different mechanisms and types of individual mechanistic transforms. These results suggest that mechanistic-level approach to yield estimation may be a useful alternative to models that are derived from full-reaction data and lack information about yield-lowering side reactions.

Nature CommunicationsVol. 15(1)
Institute for Basic Science (KR), Institute of Organic Chemistry (PL), HEM Technologies (United States) (US), Highland Community College - Illinois (US), Ulsan National Institute of Science and Technology (KR), Polish Academy of Sciences (PL)
Institute for Basic Science
Openalex Percentile: Top 25%
Machine Learning in Materials Science
0.82
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Estimation of multicomponent reactions’ yields from networks of mechanistic steps — Agnieszka Wołos, Sara Szymkuć, et al. · Nature Communications (2024) | TGRS Research Map | TGRS