Inside Front Cover: Data‐Driven Modeling of N,N’ ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions (Angew. Chem. Int. Ed. 12/2026)

Angewandte Chemie International Edition 2026 Open access

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Authors: Miao‐Jiong Tang, Tinghui Zhang, Qiuhao Huang, Shuwen Li, Rui Liu, Hongye Li, Xiaofan Chen, Shunxi Dong, Xiaohua H. Liu, Xiaoming Feng, Xin Hong
DOI: 10.1002/anie.2026-m0802020900
Cited by: 1

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.

Fields of Study & Subjects

Machine Learning in Materials Science Asymmetric Hydrogenation and Catalysis Catalytic C–H Functionalization Methods

Publication details

Journal / source
Angewandte Chemie International Edition
Publisher
Wiley
Volume
65
Issue
12
Publication date
17 February 2026
Language
EN