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


Date

2023-10-27

Publication Type

Journal Article

ETH Bibliography

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.

Publication status

published

Editor

Book title

Volume

9 (43)

Pages / Article No.

Publisher

AAAS

Event

Edition / version

Methods

Software

Geographic location

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Organisational unit

03861 - Bode, Jeffrey W. / Bode, Jeffrey W. check_circle
02514 - Laboratorium für Organische Chemie / Laboratory of Organic Chemistry

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