High-throughput synthesis predicts molecular properties and reaction outcomes
Platform leveraging photocatalysis, high-throughput experimentation & automated assays predicts synthesizability and properties of diverse drug-like molecules.
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.