Data-Integrated Elucidation of Structure–Activity Relationships toward the Rational Design of Perfluoroiodoarene-Based Halogen-Bond Donor Catalysts

Understanding and predicting catalyst performance from structural and electronic information remains a central challenge in organocatalysis. Here, we present a data-integrated framework that quantitatively combines experimental, computational, and machine learning (ML) approaches to elucidate the structure-activity relationships of halogen-bond (XB) donor catalysts based on perfluoroiodoarene cores. Single-crystal X-ray diffraction and chloride-binding analyses revealed that linker-containing two-point donors exhibit significantly stronger binding and higher catalytic activity than one-point donors. Through density functional theory calculations and an ML regression analysis integrating crystallographic and electronic descriptors, the Gibbs free energy change (Δ<i>G</i>) and binding constant (<i>K</i>) were identified as primary determinants of activity. Meanwhile, a Shapley additive explanation analysis highlighted the σ/π-hole potentials and nucleophilicity (<i>N</i> value) as additional electronic factors. This integrated experimental-computational-ML approach enables the quantitative extraction of key electronic factors governing XB donor catalysis and provides a physically interpretable framework for extending noncovalent-interaction-driven organocatalysis beyond XB formation, encompassing hydrogen- and chalcogen-bond donor systems.