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Showing content from https://pubmed.ncbi.nlm.nih.gov/31477924 below:

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Affiliations

• ¹ Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong. alex@insilico.com.
• ² Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
• ³ WuXi AppTec Co., Ltd, Shanghai, China.
• ⁴ Department of Chemistry, University of Toronto, Toronto, Ontario, Canada.
• ⁵ Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
• ⁶ Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.
• ⁷ Canadian Institute for Advanced Research, Toronto, Ontario, Canada.

• PMID: 31477924
• DOI: 10.1038/s41587-019-0224-x

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Alex Zhavoronkov et al. Nat Biotechnol. 2019 Sep.

• ¹ Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong. alex@insilico.com.
• ² Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
• ³ WuXi AppTec Co., Ltd, Shanghai, China.
• ⁴ Department of Chemistry, University of Toronto, Toronto, Ontario, Canada.
• ⁵ Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
• ⁶ Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada.
• ⁷ Canadian Institute for Advanced Research, Toronto, Ontario, Canada.

• PMID: 31477924
• DOI: 10.1038/s41587-019-0224-x

Item in Clipboard

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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• Design and Synthesis of DDR1 Inhibitors with a Desired Pharmacophore Using Deep Generative Models.

  Yoshimori A, Asawa Y, Kawasaki E, Tasaka T, Matsuda S, Sekikawa T, Tanabe S, Neya M, Natsugari H, Kanai C. Yoshimori A, et al. ChemMedChem. 2021 Mar 18;16(6):955-958. doi: 10.1002/cmdc.202000786. Epub 2021 Jan 15. ChemMedChem. 2021. PMID: 33289306 Free PMC article.

• Discovery of Pyrazolo[3,4-d]pyridazinone Derivatives as Selective DDR1 Inhibitors via Deep Learning Based Design, Synthesis, and Biological Evaluation.

  Tan X, Li C, Yang R, Zhao S, Li F, Li X, Chen L, Wan X, Liu X, Yang T, Tong X, Xu T, Cui R, Jiang H, Zhang S, Liu H, Zheng M. Tan X, et al. J Med Chem. 2022 Jan 13;65(1):103-119. doi: 10.1021/acs.jmedchem.1c01205. Epub 2021 Nov 25. J Med Chem. 2022. PMID: 34821145

• Structure-Based Design of Tetrahydroisoquinoline-7-carboxamides as Selective Discoidin Domain Receptor 1 (DDR1) Inhibitors.

  Wang Z, Bian H, Bartual SG, Du W, Luo J, Zhao H, Zhang S, Mo C, Zhou Y, Xu Y, Tu Z, Ren X, Lu X, Brekken RA, Yao L, Bullock AN, Su J, Ding K. Wang Z, et al. J Med Chem. 2016 Jun 23;59(12):5911-6. doi: 10.1021/acs.jmedchem.6b00140. Epub 2016 Jun 3. J Med Chem. 2016. PMID: 27219676 Free PMC article.

• The Commoditization of AI for Molecule Design.

  Urbina F, Ekins S. Urbina F, et al. Artif Intell Life Sci. 2022 Dec;2:100031. doi: 10.1016/j.ailsci.2022.100031. Epub 2022 Jan 24. Artif Intell Life Sci. 2022. PMID: 36211981 Free PMC article.

• Self-Driving Laboratories for Chemistry and Materials Science.

  Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, Lo S, Pablo-García S, Rajaonson EM, Skreta M, Yoshikawa N, Corapi S, Akkoc GD, Strieth-Kalthoff F, Seifrid M, Aspuru-Guzik A. Tom G, et al. Chem Rev. 2024 Aug 28;124(16):9633-9732. doi: 10.1021/acs.chemrev.4c00055. Epub 2024 Aug 13. Chem Rev. 2024. PMID: 39137296 Free PMC article. Review.

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

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