Connecting and Analyzing Enantioselective Bifunctional Hydrogen Bond Donor Catalysis Using Data Science Tools.

The generalization of related asymmetric processes in organocatalyzed reactions is an ongoing challenge due to subtle, noncovalent interactions that drive selectivity. The lack of transferability is often met with a largely empirical approach to optimizing catalyst structure and reaction conditions. This has led to the development of diverse structural catalyst motifs and inspired unique design principles in this field. Bifunctional hydrogen bond donor (HBD) catalysis exemplifies this in which a broad collection of enantioselective transformations has been successfully developed. Herein, we describe the use of data science methods to connect catalyst and substrate structural features of an array of reported enantioselective bifunctional HBD catalysis through an iterative statistical modeling process. The computational parameters used to build the correlations are mechanism-specific based on the proposed transition states, which allows for analysis into the noncovalent interactions responsible for asymmetric induction. The resulting statistical models also allow for extrapolation to out-of-sample examples to provide a prediction platform that can be used for future applications of bifunctional hydrogen bond donor catalysis. Finally, this multireaction workflow presents an opportunity to build statistical models unifying various modes of activation relevant to asymmetric organocatalysis.