Molecular recognition, binding and catalysis are often mediated by non-covalent interactions involving aromatic functional groups. Although the relative complexity of these so-called π interactions has made them challenging to study, theory and modelling have now reached the stage at which we can explain their physical origins and obtain reliable insight into their effects on molecular binding and chemical transformations. This offers opportunities for the rational manipulation of these complex non-covalent interactions and their direct incorporation into the design of small-molecule catalysts and enzymes.

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, non-covalent interactions. Little was previously understood regarding the structural origin of enantioselectivity in this system. Catalyst and substrate substituent effects were probed by 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 to optimize catalyst design.