Retrospective Analysis of Pharmaceutically Relevant Palladium-Catalyzed Cross-Coupling Reactions for Machine Learning-Guided Ligand Selection

High-throughput experimentation laboratories in industrial settings routinely generate large amounts of data in search of promising hits for further optimization. Due to their importance in C–C and C–heteroatom bond formation, palladium-catalyzed Suzuki–Miyaura and Buchwald–Hartwig reactions are routinely screened. Although significant advances have been made in this space, these screening efforts generally remain a trial-and-error endeavor. In this study, we show that leveraging historical palladium-catalyzed data sets from the high-throughput experimentation group within process chemistry at Pfizer allows the development of machine learning models capable of increasing efficiency by narrowing ligand selection through virtual screening.