Inside Front Cover: Data‐Driven Modeling of N,N’ ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions (Angew. Chem. Int. Ed. 12/2026)
Year: 2026
Type: paratext
Abstract: In the Research Article (e18560), Xiaohua Liu, Xiaoming Feng, Xin Hong, and co-workers report an integrated data platform for N,N'-dioxide/metal-catalyzed asymmetric Michael additions. Featuring over 2,000 curated reactions, it combines statistical analysis with mechanistically informed machine learning. By integrating intermediate-based augmentation and similarity-weighted tuning, the model achieves accurate selectivity prediction, validated by the successful design of a new enantioselective transformation.
Institutions: Zhejiang University, Sichuan University, Henan Normal University
Language: English
Subfield: Organic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7129430247