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

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

Research output: Contribution to journalArticlepeer-review

131 Scopus citations

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.

Original languageEnglish
Pages (from-to)737-743
Number of pages7
JournalScience
Volume347
Issue number6223
DOIs
StatePublished - 13 Feb 2015
Externally publishedYes

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