Combinatorial Explosion in Homogeneous Catalysis: Screening 60,000 Cross‐Coupling Reactions

Abstract

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.

Redox Control of Aluminum Ring-Opening Polymerization: A Combined Experimental and DFT Investigation

“…[86][87][88][89][90][91][92][93][94] and predictive studies. [95][96][97][98][99][100][101][102][103][104] To gain a better understanding of the polymerization processes discussed above, detailed theoretical studies were essential. Our group previously reported DFT calculations on redox switchable polymerization by cerium.…”

Design and Assembly of Virtual Homogeneous Catalyst Libraries –Towards in silico Catalyst Optimisation

“…The advantage is that the relationship between spaces B and C can be quantified using QSAR and quantitative structureproperty relationship (QSPR) models. [10,11] Note that space B contains molecular descriptor values, rather than structures, but these are related directly to the structures, as we showed recently for the cases of monodentate complexes in Pd-catalysed Heck reactions, [12,13] and bidentate complexes in Ni-catalysed hydrocyanation. [14] Therefore, if one can generate a sufficiently large number of sufficiently diverse structures in space A, and link the spaces A and B, one should be able to predict the relevant figures of merit in space C using QSAR/ QSPR descriptor models.…”

“…With larger datasets, more accurate mechanistic information can be inferred. [13] Limitations and Future Prospects If a direct relationship is found between the catalysts (space A), the molecular descriptors (space B), and the figures of merit (space C), then in principle it should be possible to backtrack from space C to A. Thus, if one knows the structure of a good catalyst (i.e., knows the corresponding positions in spaces B and C), it would be possible to select structures in space A that correspond to these points.…”

In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

“…[85] Linear multiple regression, neural networks and classification analysis were used to pinpoint correlations between the figures of merit of the reactions (Turnover number and Turnover frequency) and the descriptors calculated on ligand and substrate structures. Solvents were represented by empirical scales; reaction conditions such as Pd loading, time and temperature were also included in the study.…”