Linear Regression Model Development for Analysis of Asymmetric Copper-Bisoxazoline Catalysis

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.