Data Science-Driven Discovery of Optimal Conditions and a Condition-Selection Model for the Chan–Lam Coupling of Primary Sulfonamides

Abstract

Secondary N-arylsulfonamides are common in pharmaceutical compounds owing to their valuable physicochemical properties. Direct N-arylation of primary sulfonamides presents a modular approach to this scaffold but remains a challenging disconnection for transition metal-catalyzed cross coupling broadly, including the Chan–Lam (CL) coupling of nucleophiles with (hetero)aryl boronic acids. Although the CL coupling reaction typically operates under mild conditions, it is also highly substrate-dependent and prone to overarylation, limiting its generality and predictivity. To address these gaps, we employed data science tools in tandem with high-throughput experimentation to study and model the CL N-arylation of primary sulfonamides. To minimize bias in training set design, we applied unsupervised learning to systematically select a diverse set of primary sulfonamides for high-throughput data collection and modeling, resulting in a novel data set of 3,904 reactions. This workflow enabled us to identify broadly applicable, highly selective conditions for the CL coupling of aliphatic and (hetero)aromatic primary sulfonamides with complex organoboron coupling partners. We also generated a regression model that successfully identifies not only high-yielding conditions for the CL coupling of various sulfonamides but also sulfonamide features that dictate reaction outcome.