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 Y. Ivanenkov and Alex Aliper and M. Veselov and Vladimir Aladinskiy and Anastasiya V Aladinskaya and Victor A. Terentiev and Daniil Polykovskiy and M. D. Kuznetsov and Arip Asadulaev and Y. Volkov and Artem Zholus and Rim R Shayakhmetov and Alexander Zhebrak and L. Minaeva and Bogdan A Zagribelnyy and Lennart H Lee and R. Soll and David Madge and L. Xing and Tao Guo and Al{\'a}n Aspuru-Guzik},
  journal={Nature Biotechnology},
  year={2019},
  volume={37},
  pages={1038-1040}
}
  • Alex Zhavoronkov, Y. Ivanenkov, +19 authors Alán Aspuru-Guzik
  • Published 2019
  • Biology, Medicine
  • Nature Biotechnology
  • 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… CONTINUE READING

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