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Screening for generality in asymmetric catalysis

Nature. 2022 Oct;610(7933):680-686. doi: 10.1038/s41586-022-05263-2. | PubMed

Corin C Wagen1, Spencer E McMinn2, Eugene E Kwan3, Eric N Jacobsen4

  1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
  2. Discovery Chemistry, Merck & Co. Inc, Boston, MA, USA.
  3. Process Research and Development, Merck & Co. Inc, Boston, MA, USA. eugene.kwan@merck.com.
  4. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA. jacobsen@chemistry.harvard.edu.

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 products1-3. 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 synthesized4-8. 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.

Presented By Eugene E Kwan | ORCID iD