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Updated: Jan 16, 2026
Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs
Published on: January 17, 2020
Data-Driven Prediction of Enantioselectivity for the Sharpless Asymmetric Dihydroxylation: Model Development and
Blake E Ocampo1, Bilal Altundas1, Matthew J Bock1
1Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States.
None:
The Sharpless asymmetric dihydroxylation remains a key transformation in chemical synthesis, yet its success hides unexpected cases of lower selectivity. A chemoinformatic workflow was developed to allow data-driven analysis of the reaction. A database of 1007 reactions employing AD-mix α and β was curated from the literature, and an alignment-dependent, fragment-based featurization of alkenes was implemented for modeling. This platform converged on machine learning models capable of predicting the magnitude of enantioselectivity for multiple alkene classes, achieving Q 2 F3 values ≥ 0.8, test r 2 values ≥ 0.7 and mean absolute errors (MAE) ≤ 0.3 kcal/mol. The features of alkenes contributing to model performance were assessed with SHapley Additive exPlanations (SHAP) analysis to gather insight into factors underlying predictions. Experimental validation demonstrated that the models could achieve meaningful predictions on out-of-sample alkenes.
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