Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
01
11
2018
accepted:
12
07
2019
pubmed:
4
9
2019
medline:
7
11
2019
entrez:
4
9
2019
Statut:
ppublish
Résumé
We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.
Identifiants
pubmed: 31477924
doi: 10.1038/s41587-019-0224-x
pii: 10.1038/s41587-019-0224-x
doi:
Substances chimiques
Enzyme Inhibitors
0
Discoidin Domain Receptor 1
EC 2.7.10.1
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1038-1040Commentaires et corrections
Type : CommentIn
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