Data‐Driven Prediction of Reactivity and Additive Selection for C(sp2)–(Hetero)atom Bond Couplings in an Adaptive Dynamic Homogeneous Catalysis

Under visible light‐driven redox conditions, employing transition‐metal catalysis provides a powerful platform for constructing C(sp²)–(hetero)atom bonds. Although these reactions are highly significant, they require precise optimization of reaction parameters. König, Ghosh and colleagues introduced an adaptive dynamic homogeneous catalysis (AD‐HoC) platform that furnishes robust, high‐yield conditions for photocatalyzed cross‐coupling reactions. The AD‐HoC system eliminates the need to optimize catalysts, ligands, and bases; instead, it achieves C(sp²)–(hetero)atom bond coupling by merely altering additives and substrate molecules. Leveraging the predictability of reaction conditions within the AD‐HoC system, machine learning offers a method to evaluate the reactivity of substrate combinations and the categories of additives. Our research integrates high‐throughput quantum mechanical calculations with cheminformatics approaches to explore the reactivity of substrate combinations and the selection of additives within the AD‐HoC system. Further data‐driven analysis reveals that the electronic characteristics of electrophiles and the geometric characteristics of nucleophiles are key factors regulating reactivity within the AD‐HoC system. Herein, we present an end‐to‐end tool for prediction starting from the SMILES representation. This work demonstrates the collaborative use of computational statistics and machine learning to predict the reactivity and reaction conditions of substrate combinations, thereby enhancing the precision and efficiency of synthetic processes.