Experimental and computational study demonstrates enantioselective aziridination of Z-disubstituted alkenes using dirhodium metallopeptides, suggesting broader utility for catalyst modeling.
We report an enantioselective aziridination of open-chain Z-disubstituted alkenes, enabled by a dirhodium(II) metallopeptide featuring finely tuned, β-turn-biased peptidyl ligands. This method affords cis-aziridines in up to 99:1 er and exhibits broad tolerance toward diverse aryl substitution patterns and peripheral functional groups. In conjunction, we explore a data science model that uncovers patterns in molecular features that reflect enantioselectivity across two nitrene transfer reactions catalyzed by dirhodium(II) metallopeptides: the aziridination reported here and a related benzylic amination reaction. This model is trained on molecular descriptors of both catalysts and substrates and used to interpret the differences between the two reactions. By linking a new synthetic methodology to cross-reaction, data science-driven modeling, this study offers both an efficient route to valuable aziridine scaffolds and a framework for analyzing enantioinduction across mechanistically diverse transformations.
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Pighetti et al. (2026) studied this question.
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