Combinatorial explosion in homogeneous catalysis: Screening 60,000 cross-coupling reactions

A new approach to the selection of Heck cross-coupling catalysts and reaction conditions is presented, based on a quantitative structure-activity relationship (QSAR) descriptor set that is coupled to linear and non-linear analysis models. A set of steric and electronic descriptors is defined and calculated. The correlations between ligands, substrates, catalyst precursors and reaction conditions in a dataset of 412 Heck reactions are then analyzed using artificial neural networks, classification tree methods, and linear models. The regression and classification models are powerful, with prediction confidence levels as high as 93%. These models are then used to predict the performance (turnover numbers and turnover frequencies) of 60,000 combinations of virtual catalysts and reaction conditions in silico. Furthermore, the models are used to pinpoint mechanistic effects, in this case the important role that small palladium clusters play in Heck cross-coupling. The pros and cons of linear and non-linear models and the application of this approach to the selection of new experiments in high-throughput systems are discussed.