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.

Multivariate linear regression (MLR) analysis is used to unify and correlate different categories of asymmetric Cu-bisoxazoline (BOX) catalysis. The versatility of Cu-BOX complexes has been leveraged for several types of enantioselective transformations including cyclopropanation, Diels–Alder cycloadditions, and difunctionalization of alkenes. Statistical tools and extensive molecular featurization have guided the development of an inclusive linear regression model, providing a predictive platform and readily interpretable descriptors. Mechanism-specific categorization of curated data sets and parameterization of reaction components allow for simultaneous analysis of disparate organometallic intermediates such as carbenes and Lewis acid adducts, all unified by a common ligand scaffold and metal ion. Additionally, this workflow permitted the development of a complementary linear regression model correlating analogous BOX-catalyzed reactions employing Ni, Fe, Mg, and Pd complexes. Comparison of ligand parameters in each model reveals the relevant structural requirements necessary for high selectivity. Overall, this strategy highlights the utility of MLR analysis in exploring mechanistically driven correlations across a diverse chemical space in organometallic chemistry and presents an applicable workflow for related ligand classes.

A cobalt catalyst imparts a high degree of regioselectivity in the reductive cyclopropanation of polyalkenes.