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 Cubisoxazoline (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.

Automated summary

The study utilizes multivariate linear regression analysis to investigate different categories of BOX catalyzed reactions, developing predictive models for various asymmetric catalytic reactions including Cu, Ni, Fe, Mg, and Pd complexes, and reveals the structural requirements necessary for high selectivity through analysis of diverse organometallic intermediates.