Chemoinformatic Catalyst Selection Methods for the Optimization of Copper-Bis(oxazoline) Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions

Abstract

A catalyst selection method for the optimization of an asymmetric, vinylogous Mukaiyama aldol reaction is described. A large library of commercially available and synthetically accessible copper-bis(oxazoline) catalysts was constructed in silico. Conformer-dependent, grid-based descriptors were calculated for each catalyst, defining a chemical feature space suitable for machine learning. Selection of a diverse subset of catalyst space produced an initial training set of 26 novel bis(oxazoline) ligands which were synthesized and tested for stereoselectivity in the copper-catalyzed, vinylogous Mukaiyama aldol reaction for five substrate combinations. One ligand in the training set provided 88% average enantiomeric excess, exceeding the performance of catalysts identified through an initial optimization campaign. Supervised and semi-supervised catalyst selection methods, including quantitative structure-selectivity relationship modelling, nearest neighbors analysis, and a focused analogue clustering strategy, were employed to identify an additional 12 novel bis(oxazoline) ligands. The selected ligands outperformed the initial training set hit in four out of five product classes, and in some cases demonstrated excellent enantiocontrol exceeding 95% ee. The effectiveness of the unsupervised training set selection process is discussed, and the expediency of the nearest neighbor and focused analogue approaches are contrasted with the supervised quantitative structure-selectivity relationship modelling approach.

A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target

“…Taken together, the results from the evolutionary experiment suggest that multiple “islands” of high ee or TOF exist in the catalyst–substrate chemical space, and that genetic optimization “expands” them. The discontinuity of the activity/selectivity-response surface is ultimately responsible for limiting generality; 134 areas of poor performance are not simply due to structural aspects of the organocatalyst being mismatched to a particular substrates combination, 135 but rather to the electronic character of a reaction intermediate inevitably leading to slow turnover or to the disruption of some key non-covalent interactions necessary for stereoinduction.…”

Chemoinformatic Catalyst Selection Methods for the Optimization of Copper–Bis(oxazoline)-Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions

“…Feature selection was employed to produce models with decent predictive performance (R 2 = 0.72) and absolute errors (0.17 kcal/mol, Figure 4). 45 However, the feature aggregation scheme used to fit the model introduced some data leakage between train and test sets, producing a model that was expected to be overly optimistic. 46 In view of the low reliability of the supervised model, only one ligand was synthesized from the model prediction (L2, Figure 4).…”