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

Abstract

Abstract 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. A computational screening was performed to collect enantioselectivity data (ΔΔG‡: energy differences between the transition states leading to each enantiomer) via transition-state (TS) calculations using density functional theory (DFT) methods. A molecular field analysis (MFA) was carried out using the obtained calculated ΔΔG‡ values and TS structures (30 samples in total). Important structural information for enantioselectivity extracted by the MFA was visualized on the TS structures, which provided insight into an asymmetric induction mechanism. Based on the obtained information, chiral NHC ligands were designed, which showed improved enantioselectivity in these carbonyl additions (designed ligands: up to 96% ee, initial training samples: up to 73% ee).