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Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
- Source :
-
Nature biotechnology [Nat Biotechnol] 2019 Sep; Vol. 37 (9), pp. 1038-1040. Date of Electronic Publication: 2019 Sep 02. - Publication Year :
- 2019
-
Abstract
- 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.
- Subjects :
- Animals
Discoidin Domain Receptor 1 genetics
Dogs
Enzyme Inhibitors
Humans
Mice
Microsomes, Liver metabolism
Models, Molecular
Molecular Structure
Protein Conformation
Rats
Deep Learning
Discoidin Domain Receptor 1 antagonists & inhibitors
Discoidin Domain Receptor 1 metabolism
Drug Evaluation, Preclinical methods
Subjects
Details
- Language :
- English
- ISSN :
- 1546-1696
- Volume :
- 37
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- Nature biotechnology
- Publication Type :
- Academic Journal
- Accession number :
- 31477924
- Full Text :
- https://doi.org/10.1038/s41587-019-0224-x