| Literature DB >> 31477924 |
Alex Zhavoronkov1, Yan A Ivanenkov2, Alex Aliper2, Mark S Veselov2, Vladimir A Aladinskiy2, Anastasiya V Aladinskaya2, Victor A Terentiev2, Daniil A Polykovskiy2, Maksim D Kuznetsov2, Arip Asadulaev2, Yury Volkov2, Artem Zholus2, Rim R Shayakhmetov2, Alexander Zhebrak2, Lidiya I Minaeva2, Bogdan A Zagribelnyy2, Lennart H Lee3, Richard Soll3, David Madge3, Li Xing3, Tao Guo3, Alán Aspuru-Guzik4,5,6,7.
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.Entities:
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Year: 2019 PMID: 31477924 DOI: 10.1038/s41587-019-0224-x
Source DB: PubMed Journal: Nat Biotechnol ISSN: 1087-0156 Impact factor: 54.908