Predicting the Enantioselectivity of the Copper‐Catalysed Cyclopropanation of Alkenes by Using Quantitative Quadrant‐Diagram Representations of the Catalysts

Abstract

We present a new methodology to predict the enantioselectivity of asymmetric catalysis based on quantitative quadrant-diagram representations of the catalysts and quantitative structure-selectivity relationship (QSSR) modelling. To account for quadrant occupation, we used two types of molecular steric descriptors: the Taft-Charton steric parameter (ν(Charton)) and the distance-weighted volume (V(W) ). By assigning the value of the steric descriptors to each of the positions of the quadrant diagram, we generated the independent variables to build the multidimensional QSSR models. The methodology was applied to predict the enantioselectivity in the cyclopropanation of styrene catalysed by copper complexes. The dataset comprised 30 chiral ligands belonging to four different oxazoline-based ligand families: bis- (Box), azabis- (AzaBox), quinolinyl- (Quinox) and pyridyl-oxazoline (Pyox). In the first-order approximation, we generated QSSR models with good predictive ability (r(2) =0.89 and q(2) =0.88). The derived stereochemical model indicated that placing very large groups at two diagonal quadrants and leaving free the other two might be enough to obtain an enantioselective catalyst. Fitting the data to a higher-order polynomial, which included crossterms between the descriptors of the quadrants, resulted in an improvement of the predicting ability of the QSSR model (r(2) =0.96 and q(2) =0.93). This suggests that the relationship between the steric hindrance and the enantioselectivity is non-linear, and that bulky substituents in diagonal quadrants operate synergistically. We believe that the quantitative quadrant-diagram-based QSSR modelling is a further conceptual tool that can be used to predict the selectivity of chiral catalysts and other aspects of catalytic performance.