Deep Learning enables rapid identification of potent DDR1 Kinase Inhibitors. Insilico Medicine, a global leader in artificial intelligence for drug discovery, today announced the publication of a paper titled, "Deep learning enables rapid identification of potent DDR1 kinase inhibitors," in Nature Biotechnology. The paper describes a timed challenge, where the new artificial intelligence system called Generative Tensorial Reinforcement Learning (GENTRL) designed six novel inhibitors of 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.

Non-depicted distances are the same as for 3-centered pharmacophore. Non-depicted distances are the same as for 3-centered and 4-centered pharmacophores. Yellow: the reported small-molecule DDR1 inhibitor (PDB code: 5BVN).

September 2nd, 2019, 4 PM, London, Insilico Medicine, a global leader in artificial intelligence for drug discovery, today announced the publication of a paper titled, "Deep learning enables rapid identification of potent DDR1 kinase inhibitors," in Nature Biotechnology. The paper describes a timed challenge, where the new artificial intelligence system called Generative Tensorial Reinforcement Learning (GENTRL) designed six novel inhibitors of 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. The traditional drug discovery starts with the testing of thousands of small molecules in order to get to just a few lead-like molecules and only about one in ten of these molecules pass clinical trials in human patients.