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Screening for generality in asymmetric catalysis

Abstract

Research in the field of asymmetric catalysis over the past half century has resulted in landmark advances, enabling the efficient synthesis of chiral building blocks, pharmaceuticals and natural products1,2,3. A small number of asymmetric catalytic reactions have been identified that display high selectivity across a broad scope of substrates; not coincidentally, these are the reactions that have the greatest impact on how enantioenriched compounds are synthesized4,5,6,7,8. We postulate that substrate generality in asymmetric catalysis is rare not simply because it is intrinsically difficult to achieve, but also because of the way chiral catalysts are identified and optimized9. Typical discovery campaigns rely on a single model substrate, and thus select for high performance in a narrow region of chemical space. Here we put forth a practical approach for using multiple model substrates to select simultaneously for both enantioselectivity and generality in asymmetric catalytic reactions from the outset10,11. Multisubstrate screening is achieved by conducting high-throughput chiral analyses by supercritical fluid chromatography–mass spectrometry with pooled samples. When applied to Pictet–Spengler reactions, the multisubstrate screening approach revealed a promising and unexpected lead for the general enantioselective catalysis of this important transformation, which even displayed high enantioselectivity for substrate combinations outside of the screening set.

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Fig. 1: Approaches to the discovery and analysis of enantioselective reactions.
Fig. 2: Development of the SFC–MS method.
Fig. 3: High-throughput e.e. determination of enantioselective catalytic Pictet–Spengler reactions.
Fig. 4: Further reaction optimization and validation.

Data availability

All data are available in the main text or the supplementary materials.

Code availability

The Python library used for SFC–MS peak deconvolution and analysis is available on Github under the GPL 3.0 license (https://github.com/corinwagen/chromatics).

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Acknowledgements

We thank J. Gair, R. Klausen, R. Liu, A. Makarov, L. Nogle and E. Regalado for helpful discussions. This work was supported by a NIH grant GM043214 (E.N.J.), Dean’s Competitive Fund, Harvard University (E.N.J.) and NSF predoctoral fellowship DGE1745303 (C.C.W.).

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C.C.W., E.E.K. and E.N.J. conceived the work, C.C.W., S.E.M. and E.E.K. designed and validated the analytical method, C.C.W. and S.E.M. conducted the high-throughput screens. E.E.K. and E.N.J. directed the research, and all authors wrote and edited the manuscript.

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Correspondence to Eugene E. Kwan or Eric N. Jacobsen.

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Nature thanks Andrea Gargano and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Sections 1–6, including general information, details regarding analytical method development and validation text, application to the Pictet–Spengler reaction, References, NMR spectra data and further Supplementary Data. These sections include Supplementary Figs. 1–47 and Tables 1–10.

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Wagen, C.C., McMinn, S.E., Kwan, E.E. et al. Screening for generality in asymmetric catalysis. Nature 610, 680–686 (2022). https://doi.org/10.1038/s41586-022-05263-2

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