Data-Driven Modeling of N,N'-Dioxide/Metal-Catalyzed Asymmetric Michael Additions

Rational catalyst design and accurate selectivity prediction remain major challenges in asymmetric synthesis, which is critical for improving and innovating existing catalytic systems. Among them, chiral N,N'-dioxide/metal complexes have emerged as a powerful and broadly effective class of privileged catalysts, yet systematic tools for understanding and optimizing their performance remain underdeveloped. Here, we present an integrated data platform that unifies literature curation, mechanistic modeling, and predictive analytics to support intelligent catalyst selection for asymmetric N,N'-dioxide/metal-catalyzed Michael additions. We curated over 2,000 reactions from two decades of research into a chemically annotated, machine-readable dataset encompassing catalyst structure, reaction conditions, and stereochemical outcomes. This dataset enabled global statistical analyses of application patterns across metal-ligand-substrate combinations and supported a modeling framework that combines intermediate-informed data augmentation with similarity-weighted tuning, which improved predictive ability on reactions involving previously unseen substrates. Comprehensive experimental validations covering diverse substrates, ligands, and metals confirmed the model's robustness and transferability across a wide selectivity range, including the accurate identification of new highly enantioselective transformations. These findings highlight the value of data-integrated platforms in advancing the development of new reactions within complex asymmetric systems and provide an intelligent framework for future expansion of the N,N'-dioxide catalysis.