High-throughput synthesis provides data for predicting molecular properties and reaction success.

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-heterocycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on 1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereochemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and p

SUBMITTER: Gotz J 

PROVIDER: S-EPMC10610918 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature