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Deep learning enables rapid identification of potent DDR1 kinase inhibitors.

Alex ZhavoronkovYan A IvanenkovAlex AliperMark S VeselovVladimir A AladinskiyAnastasiya V AladinskayaVictor A TerentievDaniil A PolykovskiyMaksim D KuznetsovArip AsadulaevYury VolkovArtem ZholusRim R ShayakhmetovAlexander ZhebrakLidiya I MinaevaBogdan A ZagribelnyyLennart H LeeRichard SollDavid MadgeLi XingTao GuoAlán Aspuru-Guzik
Published in: Nature biotechnology (2019)
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.
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