Deep learning enables rapid identification of potent DDR1 kinase inhibitors
| Autor: | Yan A. Ivanenkov, Alex Zhavoronkov, Mark S. Veselov, Rim Shayakhmetov, Anastasiya V Aladinskaya, Daniil Polykovskiy, Victor A Terentiev, Vladimir A. Aladinskiy, Bogdan A Zagribelnyy, Tao Guo, Artem Zholus, Yury Volkov, Alexander Zhebrak, Lennart H Lee, Li Xing, Lidiya I Minaeva, Richard Soll, Alán Aspuru-Guzik, Alexander Aliper, David Madge, Maksim Kuznetsov, Arip Asadulaev |
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| Rok vydání: | 2019 |
| Předmět: |
Models
Molecular Cell signaling Protein Conformation Drug Evaluation Preclinical Biomedical Engineering Bioengineering Computational biology Biology Applied Microbiology and Biotechnology Mice Deep Learning Dogs Discoidin Domain Receptor 1 Animals Humans Enzyme Inhibitors Receptor DDR1 Molecular Structure Kinase Biological activity Molecular Pharmacology Rats Generative model Microsomes Liver Molecular Medicine Discoidin domain Biotechnology |
| Zdroj: | Nature Biotechnology. 37:1038-1040 |
| ISSN: | 1546-1696 1087-0156 |
| DOI: | 10.1038/s41587-019-0224-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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