Data-Integrated Elucidation of Structure-Activity Relationships toward the Rational Design of...

Area of Science:

• Organocatalysis
• Computational Chemistry
• Machine Learning

Background:

• Predicting catalyst performance from structural and electronic data is a major challenge in organocatalysis.
• Halogen-bond (XB) donor catalysts are crucial in various chemical transformations.

Purpose of the Study:

• To develop a data-integrated framework combining experimental, computational, and machine learning (ML) approaches.
• To elucidate the structure-activity relationships of perfluoroiodoarene-based XB donor catalysts.
• To identify key determinants of catalytic activity and electronic factors governing XB donor catalysis.

Main Methods:

• Single-crystal X-ray diffraction and chloride-binding analyses.
• Density functional theory (DFT) calculations.
• Machine learning (ML) regression analysis integrating crystallographic and electronic descriptors.
• Shapley additive explanation (SHAP) analysis.

Main Results:

• Linker-containing two-point XB donors showed stronger binding and higher catalytic activity than one-point donors.
• Gibbs free energy change (ΔG) and binding constant (K) were identified as primary determinants of activity.
• σ/π-hole potentials and nucleophilicity (N value) were highlighted as additional electronic factors.

Conclusions:

• The integrated experimental-computational-ML approach quantitatively extracts key electronic factors for XB donor catalysis.
• This framework provides a physically interpretable model for extending noncovalent-interaction-driven organocatalysis.
• The approach is applicable to hydrogen- and chalcogen-bond donor systems beyond XB formation.