Journal
SCIENCE
Volume 347, Issue 6223, Pages 737-743Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.1261043
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Funding
- NSF [CHE-0749506, CHE-1361296]
- National Institute of General Medical Sciences [R01 GM104534]
- Center for High Performance Computing at the University of Utah
- Amgen Fellowship in Organic Chemistry
- Direct For Mathematical & Physical Scien
- Division Of Chemistry [1361296] Funding Source: National Science Foundation
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1341935] Funding Source: National Science Foundation
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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.
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