Predicting Success in Cu-Catalyzed C–N Coupling Reactions using Data Science

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Herein, we report an end-to-end data driven process to effectively predict how structural features of coupling partners and ligands impact Cu-catalyzed C–N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands, while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.