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Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
Open accessAbstract
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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“Other 3D-focused approaches include the development of maps of electrostatics with probe molecules (71), an approach that has recently gained favor in deep learning models for predicting gas storage characteristics of MOFs (80), or experimental homogeneous catalyst activities (81), where it is sometimes called stereocartography (82).”
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Metadata
- 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.
- Venue
- Science
- Published
- 2019-01-18
- Volume
- 363
- Issue
- 6424
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- 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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