Cited by 684Asymmetric Synthesis and CatalysisMachine Learning in Materials ScienceAsymmetric Hydrogenation and Catalysis

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Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

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Zahrt, Andrew F., Henle, Jeremy J., Rose, Brennan T., Wang, Yang, Darrow, William T., Denmark, Scott E.·2019·Science

DOI 10.1126/science.aau5631PMC PMC6417887

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Abstract

Predicting catalyst selectivity: Asymmetric catalysis is widely used in chemical research and manufacturing to access just one of two possible mirror-image products. Nonetheless, the process of tuning catalyst structure to optimize selectivity is still largely empirical. Zahrt et al. present a framework for more efficient, predictive optimization. As a proof of principle, they focused on a known coupling reaction of imines and thiols catalyzed by chiral phosphoric acid compounds. By modeling multiple conformations of more than 800 prospective catalysts, and then training machine-learning algorithms on a subset of experimental results, they achieved highly accurate predictions of enantioselectivities. Science , this issue p. eaau5631

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Authors: Zahrt, A. F., Henle, J. J., Rose, B. T., Wang, Y., Darrow, W. T., & Denmark, S. E.
Affiliations: University of Illinois Urbana-Champaign (US)
Corresponding author: Zahrt, Andrew F.
Henle, Jeremy J.
Denmark, Scott E.
ORCID: Zahrt, Andrew F.
Henle, Jeremy J.
Rose, Brennan T.
Wang, Yang
Darrow, William T.
Denmark, Scott E.
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2019-01-18
Volume: 363
Issue: 6424
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.aau5631
PMC: PMC6417887
OpenAlex: W2910222073
Cited by: 683
References: 90
Citations by year: 2026: 91 · 2025: 135 · 2024: 97 · 2023: 93 · 2022: 99 · 2021: 88 · 2020: 55 · 2019: 25
FWCI: 26.17
Type: journal article
Language: en
Open access: green
europepmc.org
Locations: Science (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: W.M. Keck Foundation
Janssen Research Development LLC, San Diego
Topics: Physical Sciences › Chemistry › Organic Chemistry › Asymmetric Synthesis and Catalysis
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Chemistry › Inorganic Chemistry › Asymmetric Hydrogenation and Catalysis
Keywords: asymmetric catalysis, enantioselectivity prediction, chiral phosphoric acid catalysts, machine learning

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Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

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