Molecular Field Analysis Using Computational-Screening Data in Asymmetric <i>N</i>-Heterocyclic Carbene-Copper Catalysis toward Data-Driven <i>In Silico</i> Catalyst Optimization

A molecular-field-based regression analysis using computational screening data for N-heterocyclic carbene (NHC)-Cu-catalyzed asymmetric carbonyl additions of a silylboronate to aldehydes is reported. was performed collect enantioselectivity (ΔΔG‡: energy differences between the transition states leading each enantiomer) via transition-state (TS) calculations density functional theory (DFT) methods. molecular field (MFA) carried out obtained calculated ΔΔG‡ values and TS structures (30 samples in total). Important structural information extracted by MFA visualized on structures, which provided insight into an induction mechanism. Based information, chiral NHC ligands were designed, showed improved these (designed ligands: up 96% ee, initial training samples: 73% ee). computational-screening corresponding with relatively small dataset samples) enabled regression-based data-driven design Cu-catalyzed addition silylboronate.