Deep learning enables rapid identification of potent DDR1 kinase inhibitors

@article{Zhavoronkov2019DeepLE,
  title={Deep learning enables rapid identification of potent DDR1 kinase inhibitors},
  author={Alex Zhavoronkov and Yan A. Ivanenkov and Alexander Aliper and Mark Veselov and Vladimir Aladinskiy and Anastasiya V Aladinskaya and Victor Terentiev and Daniil Polykovskiy and Maksim Kuznetsov and Arip Asadulaev and Yury Volkov and Artem Zholus and Shayakhmetov Rim and Alexander Zhebrak and Lidiya I. Minaeva and Bogdan Zagribelnyy and Lennart H Lee and Richard M. Soll and David Madge and Li Xing and Tao Guo and Al{\'a}n Aspuru-Guzik},
  journal={Nature Biotechnology},
  year={2019},
  volume={37},
  pages={1038-1040}
}
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… Expand

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