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

  • Andrew F. Zahrt
    Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
  • Jeremy J. Henle
    Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
  • Brennan T. Rose
    Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
  • Yang Wang
    Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
  • William T. Darrow
    Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.
  • Scott E. Denmark
    Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.

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<jats:title>Predicting catalyst selectivity</jats:title> <jats:p> 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 <jats:italic>et al.</jats:italic> 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. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" related-article-type="in-this-issue" xlink:href="10.1126/science.aau5631">eaau5631</jats:related-article> </jats:p>

収録刊行物

  • Science

    Science 363 (6424), eaau5631-, 2019-01-18

    American Association for the Advancement of Science (AAAS)

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