Molecular Field Analysis Using Computational-Screening Data in Asymmetric N-Heterocyclic Carbene-Copper Catalysis toward Data-driven in silico Catalyst Optimization

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 (DDG ‡ : energy differences between the transitions states leading to each enantiomer) via transitionstate (TS) calculations using density functional theory (DFT) methods. A molecular field analysis (MFA) was carried out using the obtained calculated DDG ‡ 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.

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors

“…10 These methods have already been applied in asymmetric catalysis, using calculated 3D structures with grid-based binary feature vectors, such as Comparative Molecular Field Analysis (CoMFA). [11][12][13][14][15] The Denmark group reported a modification of grid-based methods with non-binary features based on distribution of conformations to account for their flexibility, and demonstrated that the model can predict higher selectivity even in the absence of such examples in the training dataset. 16 The disadvantage of such methods is the requirement of costly quantum chemical calculations and, in the case of grid-based methods, the necessity of alignment of core structures.…”