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Milo A, Neel AJ, Toste FD, et al. Organic chemistry. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis. Science. 2015;347(6223):737-43doi: 10.1126/science.1261043.
Milo, A., Neel, A. J., Toste, F. D., & Sigman, M. S. (2015). Organic chemistry. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis. Science (New York, N.Y.), 347(6223), 737-43. https://doi.org/10.1126/science.1261043
Milo, Anat, et al. "Organic chemistry. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis." Science (New York, N.Y.) vol. 347,6223 (2015): 737-43. doi: https://doi.org/10.1126/science.1261043
Milo A, Neel AJ, Toste FD, Sigman MS. Organic chemistry. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis. Science. 2015 Feb 13;347(6223):737-43. doi: 10.1126/science.1261043. PMID: 25678656; PMCID: PMC4465137.
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Science. 2015 Feb 13;347(6223):737-43. doi: 10.1126/science.1261043.

Organic chemistry. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis.

Science (New York, N.Y.)

Anat Milo, Andrew J Neel, F Dean Toste, Matthew S Sigman

Affiliations

  1. Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, UT 84112, USA.
  2. Chemical Sciences Division, Lawrence Berkeley National Laboratory, and Department of Chemistry, University of California, Berkeley, CA 94720, USA.
  3. Chemical Sciences Division, Lawrence Berkeley National Laboratory, and Department of Chemistry, University of California, Berkeley, CA 94720, USA. [email protected] [email protected].
  4. Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, UT 84112, USA. [email protected] [email protected].

PMID: 25678656 PMCID: PMC4465137 DOI: 10.1126/science.1261043

Abstract

Knowledge of chemical reaction mechanisms can facilitate catalyst optimization, but extracting that knowledge from a complex system is often challenging. Here, we present a data-intensive method for deriving and then predictively applying a mechanistic model of an enantioselective organic reaction. As a validating case study, we selected an intramolecular dehydrogenative C-N coupling reaction, catalyzed by chiral phosphoric acid derivatives, in which catalyst-substrate association involves weak, noncovalent interactions. Little was previously understood regarding the structural origin of enantioselectivity in this system. Catalyst and substrate substituent effects were probed by means of systematic physical organic trend analysis. Plausible interactions between the substrate and catalyst that govern enantioselectivity were identified and supported experimentally, indicating that such an approach can afford an efficient means of leveraging mechanistic insight so as to optimize catalyst design.

Copyright © 2015, American Association for the Advancement of Science.

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