Holistic prediction of enantioselectivity in asymmetric catalysis

Abstract

When faced with unfamiliar reaction space, synthetic chemists typically apply the reported conditions (reagents, catalyst, solvent and additives) of a successful reaction to a desired, closely related reaction using a new substrate type. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Consequently, an important goal in synthetic chemistry is the ability to transfer chemical observations quantitatively from one reaction to another. Here we present a holistic, data-driven workflow for deriving statistical models of one set of reactions that can be used to predict out-of-sample reactions. As a validating case study, we combined published enantioselectivity datasets that employ 1,1′-bi-2-naphthol (BINOL)-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines and developed statistical models. These models reveal the general interactions that impart asymmetric induction and allow the quantitative transfer of this information to new reaction components. This technique creates opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.

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Fig. 1: Workflow for interrogating and applying mechanistic transferability.
Fig. 2: Comprehensive model development.
Fig. 3: Development of focused correlations.
Fig. 4: Out-of-sample predictions using two-tiered prediction workflow.

Data availability

All data relating to this study is available in the Supplementary Information.

Code availability

All code used for model development is available in the Supplementary Information.

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Acknowledgements

J.P.R. thanks the EU Horizon 2020 Marie Skłodowska-Curie Fellowship (grant 792144) and M.S.S. thanks the NIH (grant GM-121383) for support of this work. Computational resources were provided from the Center for High Performance Computing (CHPC) at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the NSF (grant ACI-1548562) and provided through allocation TG-CHE180003.

Author information

J.P.R. designed and performed all computations and statistical analyses. Both authors contributed to the analysis and writing of the manuscript.

Correspondence to Matthew S. Sigman.

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Extended data figures and tables

Extended Data Fig. 1 Reaction component comparison.

Parameterization challenges for the identification of numerical descriptors in reaction dimension, demonstrated using two reactions that represent the extremes of multidimensional feature space. MS, molecular sieves.

Supplementary information

Supplementary Information

This file contains a full list of authors in the Gaussian 09 reference; Computational Methods; Cartesian Coordinates of all the Substrate, Catalyst and Solvent Structures; Collected Parameters; Data Curation; Model Development and Supplementary References.

Supplementary Table 1

This file contains the parameter tables.

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