Combining generative artificial intelligence and on-chip synthesis for de novo drug design.
Journal
Science advances
ISSN: 2375-2548
Titre abrégé: Sci Adv
Pays: United States
ID NLM: 101653440
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
27
12
2020
accepted:
23
04
2021
entrez:
12
6
2021
pubmed:
13
6
2021
medline:
9
4
2022
Statut:
epublish
Résumé
Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.
Identifiants
pubmed: 34117066
pii: 7/24/eabg3338
doi: 10.1126/sciadv.abg3338
pmc: PMC8195470
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
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