High-throughput synthesis predicts molecular properties and reaction outcomes

Abstract

The generation of attractive scaffolds for drug discovery efforts requires the expeditious synthesis of diverse analogues from readily available building blocks. This endeavor necessitates a trade-off between diversity and ease of access and is further complicated by uncertainty about the synthesizability and pharmacokinetic properties of the resulting compounds. Here, we document a platform that leverages photocatalytic N-hetero-cycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereo-chemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and properties. This study exemplifies how organic chemists can exploit state-of-the-art technologies to markedly increase throughput and confidence in the preparation of drug-like molecules.