High-Level Data Fusion Enables the Chemoinformatically Guided Discovery of Chiral Disulfonimide Catalysts for Atropselective Iodination of 2-Amino-6-arylpyridines

Brennan T. Rose, Jacob C. Timmerman, Seth A. Bawel, Steven Chin, Haiming Zhang, Scott E. Denmark

Research output: Contribution to journalArticlepeer-review

Abstract

The atropselective iodination of 2-amino-6-arylpyridines catalyzed by chiral disulfonimides (DSIs) is described. Key to the development of this transformation was the use of a chemoinformatically guided workflow for the curation of a structurally diverse training set of DSI catalysts. Utilization of this catalyst training set in the atropselective iodination across a variety 2-aminopyridine substrates allowed for the recommendation of statistically higher-performing DSIs for this reaction. Data Fusion techniques were implemented to successfully predict the performance of catalysts when classical linear regression analysis failed to provide suitable models. This effort identified a privileged class of 3,3′-alkynyl-DSI catalysts which were effective in catalyzing the iodination of a variety of 2-amino-6-arylpyridines with high stereoselectivity and generality. Subsequent preparative-scale demonstrations highlighted the utility of this reaction by providing iodinated pyridines >90:10 er and in good chemical yield.

Original languageEnglish (US)
Pages (from-to)22950-22964
Number of pages15
JournalJournal of the American Chemical Society
Volume144
Issue number50
DOIs
StatePublished - Dec 21 2022

ASJC Scopus subject areas

  • General Chemistry
  • Biochemistry
  • Catalysis
  • Colloid and Surface Chemistry

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