Inside Front Cover: Data‐Driven Modeling of <i>N,N’</i> ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions (Angew. Chem. Int. Ed. 12/2026)
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.
Authors
- Miao‐Jiong Tang (ORCID: https://orcid.org/0000-0003-2075-366X)
- Xiaohua H. Liu
- Shuwen Li (ORCID: https://orcid.org/0000-0002-7867-908X)
- Xin Hong
- Xiaoming Feng (ORCID: https://orcid.org/0000-0002-7743-0952)
- Hongye Li (ORCID: https://orcid.org/0000-0001-5180-1754)
- Qian Huang (ORCID: https://orcid.org/0000-0002-2234-6024)
- Tinghui Zhang
- Shunxi Dong
- Xiaofan Chen (ORCID: https://orcid.org/0000-0002-4508-5971)
- Rui Liu
Institutions
- Sichuan University (CN)
- Henan Normal University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Angewandte Chemie International Edition
- Published
- 2026-02-17
- DOI
- https://doi.org/10.1002/anie.2026-m0802020900
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00