title: Organic chemistry. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis.
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.
authors: Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, UT 84112, USA.
authors: Anat
authors: A
authors: Milo
authors: Chemical Sciences Division, Lawrence Berkeley National Laboratory, and Department of Chemistry, University of California, Berkeley, CA 94720, USA.
authors: Andrew J
authors: AJ
authors: Neel
authors: Chemical Sciences Division, Lawrence Berkeley National Laboratory, and Department of Chemistry, University of California, Berkeley, CA 94720, USA. sigman@chem.utah.edu fdtoste@berkeley.edu.
authors: F Dean
authors: FD
authors: Toste
authors: Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, UT 84112, USA. sigman@chem.utah.edu fdtoste@berkeley.edu.
authors: Matthew S
authors: MS
authors: Sigman