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 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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000640106Publication status
publishedExternal links
Journal / series
Science AdvancesVolume
Pages / Article No.
Publisher
AAASOrganisational unit
02514 - Laboratorium für Organische Chemie / Laboratory of Organic Chemistry03861 - Bode, Jeffrey W. / Bode, Jeffrey W.
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