Inside Front Cover: Data‐Driven Modeling of N,N’ ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions

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.