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Comparing quantitative prediction methods for the discovery of small-molecule chiral catalysts

Abstract

Advances in density functional theory (DFT) mean that it is now possible to study catalytic reactions with sufficient accuracy that the results compare favourably with experiment. These high-level calculations have been applied to understand and predict variations in catalytic performance from one catalyst to another, but can require substantial computational resources. By contrast, multivariate linear regression (MLR) methods are rapidly becoming versatile, statistical tools for predicting and understanding the roles of catalysts and substrates and act as a useful complement to complex transition state calculations, with a substantially lower computational cost. Herein, we compare these approaches, DFT calculations and data analysis techniques, and discuss their ability to provide meaningful predictions of catalyst performance. Examples of applications are selected to demonstrate the advantages and limitations of both tools. Several ongoing challenges in the predictions of reaction outcomes are also highlighted.

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Fig. 1: Computationally led design of a selective organocatalyst for an anti-Mannich reaction19.
Fig. 2: Computationally led design of a selective simplified thiourea for a Michael cyclization22.
Fig. 3: Computationally led design of a selective simplified organocatalyst for a Michael addition23.
Fig. 4: Design of a superior phosphoramidite-complexed rhodium catalyst37
Fig. 5: Development of 3D-LFER for correlating and optimizing enantioselectivity in Nozaki–Hiyama–Kishi reactions68,72.
Fig. 6: Probing the interdependence of substrate and catalyst effects using Sterimol parameters in a Nozaki–Hiyama–Kishi-type propargylation81.
Fig. 7: Prediction of improved catalysts for the dehydrogenative Heck arylation with cis-alkenols86.
Fig. 8: The development and application of non-covalent parameters probing attractive interactions91.
Fig. 9: Correlating reaction figures of merit (yield95, rate96, turnover frequency97 and oxidation potential98) with empirical and/or theoretical measurements of catalyst structure.
Fig. 10: Using modelling approaches to facilitate mechanistic studies of challenging catalyst and reaction classes.

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Acknowledgements

This effort and associated research were supported by the National Science Foundation (NSF) (CHE-1361296), US Army Research Office Multidisciplinary University Research Initiative (MURI) (#W911NF1410263) and the US National Institutes of Health (NIH) (1 R01 GM121383). The support and resources from the Center for High Performance Computing at the University of Utah are gratefully acknowledged. M.S.S. acknowledges the excellent contributions from current and past group members as well as the exceptional group of collaborators. The authors are grateful to R. Paton for providing coordinate files of the relevant transition states.

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Reid, J.P., Sigman, M.S. Comparing quantitative prediction methods for the discovery of small-molecule chiral catalysts. Nat Rev Chem 2, 290–305 (2018). https://doi.org/10.1038/s41570-018-0040-8

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