High-throughput synthesis provides data for predicting molecular properties and reaction success
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Date
2023-10-27
Publication Type
Journal Article
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yes
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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.
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published
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Volume
9 (43)
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Publisher
AAAS
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Software
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Organisational unit
03861 - Bode, Jeffrey W. / Bode, Jeffrey W.
02514 - Laboratorium für Organische Chemie / Laboratory of Organic Chemistry