Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Nat Biotechnol. 2019 Sep;37(9):1038-1040. doi: 10.1038/s41587-019-0224-x. Epub 2019 Sep 2.


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.

MeSH terms

  • Animals
  • Deep Learning*
  • Discoidin Domain Receptor 1 / antagonists & inhibitors*
  • Discoidin Domain Receptor 1 / genetics
  • Discoidin Domain Receptor 1 / metabolism*
  • Dogs
  • Drug Evaluation, Preclinical / methods*
  • Enzyme Inhibitors
  • Humans
  • Mice
  • Microsomes, Liver / metabolism
  • Models, Molecular
  • Molecular Structure
  • Protein Conformation
  • Rats


  • Enzyme Inhibitors
  • Discoidin Domain Receptor 1