Literature DB >> 31477924

Deep learning enables rapid identification of potent DDR1 kinase inhibitors.

Alex Zhavoronkov1, Yan A Ivanenkov2, Alex Aliper2, Mark S Veselov2, Vladimir A Aladinskiy2, Anastasiya V Aladinskaya2, Victor A Terentiev2, Daniil A Polykovskiy2, Maksim D Kuznetsov2, Arip Asadulaev2, Yury Volkov2, Artem Zholus2, Rim R Shayakhmetov2, Alexander Zhebrak2, Lidiya I Minaeva2, Bogdan A Zagribelnyy2, Lennart H Lee3, Richard Soll3, David Madge3, Li Xing3, Tao Guo3, Alán Aspuru-Guzik4,5,6,7.   

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.

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Year:  2019        PMID: 31477924     DOI: 10.1038/s41587-019-0224-x

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  130 in total

Review 1.  Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.

Authors:  Kevin Maik Jablonka; Daniele Ongari; Seyed Mohamad Moosavi; Berend Smit
Journal:  Chem Rev       Date:  2020-06-10       Impact factor: 60.622

Review 2.  Generative chemistry: drug discovery with deep learning generative models.

Authors:  Yuemin Bian; Xiang-Qun Xie
Journal:  J Mol Model       Date:  2021-02-04       Impact factor: 1.810

Review 3.  Recent progress on cheminformatics approaches to epigenetic drug discovery.

Authors:  Zoe Sessions; Norberto Sánchez-Cruz; Fernando D Prieto-Martínez; Vinicius M Alves; Hudson P Santos; Eugene Muratov; Alexander Tropsha; José L Medina-Franco
Journal:  Drug Discov Today       Date:  2020-09-30       Impact factor: 7.851

4.  Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.

Authors:  Brent M Kuenzi; Jisoo Park; Samson H Fong; Kyle S Sanchez; John Lee; Jason F Kreisberg; Jianzhu Ma; Trey Ideker
Journal:  Cancer Cell       Date:  2020-10-22       Impact factor: 31.743

Review 5.  Rethinking drug design in the artificial intelligence era.

Authors:  Petra Schneider; W Patrick Walters; Alleyn T Plowright; Norman Sieroka; Jennifer Listgarten; Robert A Goodnow; Jasmin Fisher; Johanna M Jansen; José S Duca; Thomas S Rush; Matthias Zentgraf; John Edward Hill; Elizabeth Krutoholow; Matthias Kohler; Jeff Blaney; Kimito Funatsu; Chris Luebkemann; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2019-12-04       Impact factor: 84.694

6.  Deep learning of pharmacogenomics resources: moving towards precision oncology.

Authors:  Yu-Chiao Chiu; Hung-I Harry Chen; Aparna Gorthi; Milad Mostavi; Siyuan Zheng; Yufei Huang; Yidong Chen
Journal:  Brief Bioinform       Date:  2020-12-01       Impact factor: 11.622

Review 7.  Decoding disease: from genomes to networks to phenotypes.

Authors:  Aaron K Wong; Rachel S G Sealfon; Chandra L Theesfeld; Olga G Troyanskaya
Journal:  Nat Rev Genet       Date:  2021-08-02       Impact factor: 53.242

8.  Deep learning to design nuclear-targeting abiotic miniproteins.

Authors:  Carly K Schissel; Somesh Mohapatra; Justin M Wolfe; Colin M Fadzen; Kamela Bellovoda; Chia-Ling Wu; Jenna A Wood; Annika B Malmberg; Andrei Loas; Rafael Gómez-Bombarelli; Bradley L Pentelute
Journal:  Nat Chem       Date:  2021-08-09       Impact factor: 24.427

9.  Prediction of drug efficacy from transcriptional profiles with deep learning.

Authors:  Jie Zhu; Jingxiang Wang; Xin Wang; Mingjing Gao; Bingbing Guo; Miaomiao Gao; Jiarui Liu; Yanqiu Yu; Liang Wang; Weikaixin Kong; Yongpan An; Zurui Liu; Xinpei Sun; Zhuo Huang; Hong Zhou; Ning Zhang; Ruimao Zheng; Zhengwei Xie
Journal:  Nat Biotechnol       Date:  2021-06-17       Impact factor: 54.908

Review 10.  Recent advances in drug repurposing using machine learning.

Authors:  Fabio Urbina; Ana C Puhl; Sean Ekins
Journal:  Curr Opin Chem Biol       Date:  2021-07-16       Impact factor: 8.822

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