Using Data Science to Interrogate Transition State Stabilization in Anion-Binding Catalysis for Disulfide Ring-Opening Polymerization

In nature, enzymes leverage constituent amino acid residues to create catalytic microenvironment for their active sites to effect high reactivity and selectivity. Multicomponent host−guest assemblies have been exploited to mimic enzymatic microenvironments by preorganizing a network of noncovalent interactions. While anion-binding catalysts such as thioureas have gained widespread success in organic transformation and controlled polymerization, evaluation of the participating structural features in the transition state (TS) stabilization remains challenging. Herein, we report the use of data science tools, i.e., a decision-tree-based machine-learning algorithm and Shapley additive explanations (SHAP) analysis, to model reactivity and regioselectivity in a thiourea-catalyzed ring-opening polymerization of 1,2-dithiolanes. Variation of aryl substituent position and electronic characteristics helps reveal key catalyst features involved in the TS stabilization within the catalytically active, multicomponent host−guest complexes. The analysis of feature importance helps explain the reason behind the optimal performance of (pseudo)halogen-substituted catalysts. Furthermore, the structural basis for the unveiled reactivity-regioselectivity trade-off in the catalysis are established.