Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Sep;37(9):1038-1040.
doi: 10.1038/s41587-019-0224-x. Epub 2019 Sep 2.

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Alex Zhavoronkov  1 , Yan A Ivanenkov  2 , Alex Aliper  2 , Mark S Veselov  2 , Vladimir A Aladinskiy  2 , Anastasiya V Aladinskaya  2 , Victor A Terentiev  2 , Daniil A Polykovskiy  2 , Maksim D Kuznetsov  2 , Arip Asadulaev  2 , Yury Volkov  2 , Artem Zholus  2 , Rim R Shayakhmetov  2 , Alexander Zhebrak  2 , Lidiya I Minaeva  2 , Bogdan A Zagribelnyy  2 , Lennart H Lee  3 , Richard Soll  3 , David Madge  3 , Li Xing  3 , Tao Guo  3 , Alán Aspuru-Guzik  4   5   6   7
Affiliations

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Alex Zhavoronkov et al. Nat Biotechnol. 2019 Sep.

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.

PubMed Disclaimer

Comment in

References

    1. Paul, S. M. et al. Nat. Rev. Drug Discov. 9, 203–214 (2010). - DOI
    1. Avorn, J. N. Engl. J. Med. 372, 1877–1879 (2015). - DOI
    1. Goodfellow, I. et al. Generative adversarial nets. in Advances in Neural Information Processing Systems 2672–2680 (2014).
    1. Mamoshina, P. et al. Mol. Pharm. 13, 1445–1454 (2016). - DOI
    1. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Science 361, 360–365 (2018). - DOI

Substances