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.