Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

In this study, we introduce an approach for predicting the enantioselectivity of P-chiral monophosphorus ligands from ligand-based descriptors that can be applied to catalytic systems with small experimental datasets without reliance on mechanistic knowledge. Principal component analysis (PCA) is used to map out the chemical space described by steric and electronic descriptors computed for dihydrobenzooxaphosphole (BOP) and dihydrobenzoazaphosphole (BAP) ligands. The PCA map captures trends in the experimentally measured enantioselectivity of four C–C bond-forming reactions and identifies “hotspots” of selective ligands that provide insight into the optimal balance of sterics and electronics for each reaction. Furthermore, the descriptors are used to train a ridge regression model that quantitatively predicts the enantioselectivity of a Pd-catalyzed Negishi cross-coupling reaction. The coefficients of the model provide fundamental chemical understanding and reveal that a π-stacking interaction with one of the ligands results in an unexpected selectivity inversion. Overall, this integrated approach combines ligand-based descriptors with small experimental datasets to provide qualitative (PCA) and quantitative (ridge regression) enantioselectivity predictions.