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Data-Driven Predictions of Diastereoselectivity in Crystallization-Induced Diastereomer Transformations
Sara L McCormack1, Emily R Sherman2, Jeffrey S Johnson2
1Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States.
None:
Crystallization-induced diastereomer transformations (CIDTs) enable labile stereogenic center control but remain difficult to predict due to poorly understood structure-selectivity relationships. We report a workflow to featurize product structures and construct statistical models to predict diastereoselectivity, validating the model on six previously untested substrates and achieving predictive accuracy. Feature analysis highlights amide identity, conformational compactness, and local electronic properties as key determinants of stereochemical outcome, providing a mechanistic basis and a predictive framework for CIDT design.
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