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, and Scott E. Denmark
Journal of the American Chemical Society 2022 144 (50), 22950-22964

DOI: 10.1021/jacs.2c08820


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.


The data and code used for descriptor calculations and modeling can be found at