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

Optimizing a catalyst many ways at once Optimization strategies are often likened to hikes in a hilly landscape. If your goal is to get to the top of the highest hill, and you only take steps toward higher ground, you might never find a peak on a route that requires a preliminary descent. So it is in chemistry, where optimizing each structural feature of a catalyst consecutively might gloss over subtle tradeoffs that in combination offer the best performance. Milo et al. use multidimensional analysis techniques to generate a predictive model of how selectivity depends on multiple characteristics of the catalyst and substrate in a C-N bond-forming reaction (see the Perspective by Lu). They then apply this model to improve the catalyst globally. Science, this issue p. 737; see also p. 719 Multidimensional analysis of a complex reaction offers predictive insight for rational catalyst optimization. [Also see Perspective by Lu] 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.