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Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling
[Image: see text] Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that abundance of carefully curated li...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949728/ https://www.ncbi.nlm.nih.gov/pubmed/35258973 http://dx.doi.org/10.1021/jacs.1c12005 |
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author | Beker, Wiktor Roszak, Rafał Wołos, Agnieszka Angello, Nicholas H. Rathore, Vandana Burke, Martin D. Grzybowski, Bartosz A. |
author_facet | Beker, Wiktor Roszak, Rafał Wołos, Agnieszka Angello, Nicholas H. Rathore, Vandana Burke, Martin D. Grzybowski, Bartosz A. |
author_sort | Beker, Wiktor |
collection | PubMed |
description | [Image: see text] Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki–Miyaura coupling with heterocyclic building blocks—and a carefully selected database of >10,000 literature examples—we show that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases. This result holds irrespective of the ML model applied (from simple feed-forward to state-of-the-art graph-convolution neural networks) or the representation to describe the reaction partners (various fingerprints, chemical descriptors, latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on the sheer frequency of certain reaction conditions reported in the literature. These unsatisfactory results likely reflect subjective preferences of various chemists to use certain protocols, other biasing factors as mundane as availability of certain solvents/reagents, and/or a lack of negative data. These findings highlight the likely importance of systematically generating reliable and standardized data sets for algorithm training. |
format | Online Article Text |
id | pubmed-8949728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-89497282022-03-28 Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling Beker, Wiktor Roszak, Rafał Wołos, Agnieszka Angello, Nicholas H. Rathore, Vandana Burke, Martin D. Grzybowski, Bartosz A. J Am Chem Soc [Image: see text] Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki–Miyaura coupling with heterocyclic building blocks—and a carefully selected database of >10,000 literature examples—we show that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases. This result holds irrespective of the ML model applied (from simple feed-forward to state-of-the-art graph-convolution neural networks) or the representation to describe the reaction partners (various fingerprints, chemical descriptors, latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on the sheer frequency of certain reaction conditions reported in the literature. These unsatisfactory results likely reflect subjective preferences of various chemists to use certain protocols, other biasing factors as mundane as availability of certain solvents/reagents, and/or a lack of negative data. These findings highlight the likely importance of systematically generating reliable and standardized data sets for algorithm training. American Chemical Society 2022-03-08 2022-03-23 /pmc/articles/PMC8949728/ /pubmed/35258973 http://dx.doi.org/10.1021/jacs.1c12005 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Beker, Wiktor Roszak, Rafał Wołos, Agnieszka Angello, Nicholas H. Rathore, Vandana Burke, Martin D. Grzybowski, Bartosz A. Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling |
title | Machine
Learning May Sometimes Simply Capture Literature
Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura
Coupling |
title_full | Machine
Learning May Sometimes Simply Capture Literature
Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura
Coupling |
title_fullStr | Machine
Learning May Sometimes Simply Capture Literature
Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura
Coupling |
title_full_unstemmed | Machine
Learning May Sometimes Simply Capture Literature
Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura
Coupling |
title_short | Machine
Learning May Sometimes Simply Capture Literature
Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura
Coupling |
title_sort | machine
learning may sometimes simply capture literature
popularity trends: a case study of heterocyclic suzuki–miyaura
coupling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949728/ https://www.ncbi.nlm.nih.gov/pubmed/35258973 http://dx.doi.org/10.1021/jacs.1c12005 |
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