ArticleACS central science2025
Data-Driven Prediction of Enantioselectivity for the Sharpless Asymmetric Dihydroxylation: Model Development and Experimental Validation.
Article in ACS central science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- Predicting Enantioselectivity of Ruthenium-Catalyzed Ketone Hydrogenation with 3D Structure-Based Deep Learning.Journal of the American Chemical Society · 2026Article
- Accelerating Reaction Discovery through AI-HTE Integration: Nitrene-Mediated C-O Coupling as a Validated Case Study.JACS Au · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
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