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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Falivene, L., Cao, Z., Petta, A., Serra, L., Poater, A., Oliva, R., Scarano, V., & Cavallo, L. (2019) · Nature Chemistry · doi:10.1038/s41557-019-0319-5 · Cited by 1,290
CO2 hydrogenation to high-value products via heterogeneous catalysis
Ye, R., Ding, J., Gong, W., Argyle, M. D., Zhong, Q., Wang, Y., Russell, C. K., Xu, Z., Russell, A. G., Li, Q., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13638-9 · Cited by 1,207
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DScribe: Library of descriptors for machine learning in materials science
Himanen, L., Jäger, M. O. J., Morooka, E. V., Canova, F. F., Ranawat, Y. S., Gao, D. Z., Rinke, P., & Foster, A. S. (2019) · Computer Physics Communications · doi:10.1016/j.cpc.2019.106949 · Cited by 901
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Late-stage C–H functionalization offers new opportunities in drug discovery
Guillemard, L., Kaplaneris, N., Ackermann, L., & Johansson, M. J. (2021) · Nature Reviews Chemistry · doi:10.1038/s41570-021-00300-6 · Cited by 845

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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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