Journal
JOURNAL OF MEDICINAL CHEMISTRY
Volume 63, Issue 16, Pages 8723-8737Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jmedchem.9b00855
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Funding
- National Natural Science Foundation of China [81773634, 81430084]
- National Science & Technology Major Project Key New Drug Creation and Manufacturing Program, China [2018ZX09711002]
- Personalized Medicines.Molecular SignatureBased Drug Discovery and Development, Strategic Priority Research Program of the Chinese Academy of Sciences [XDA12050201]
- Open Fund of State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, China [KF-GN-201706]
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The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm. The obtained model yields excellent internal prediction capability with an auROC of 0.90 and consistently outperforms conventional single-task models on external tests, especially for kinases with insufficient activity data. Moreover, more rigorous experimental validations including 1410 kinase-compound pairs showed a high-quality average auROC of 0.75 and confirmed many novel predicted off-target activities. Given the verified generalizability, the model was further applied to various scenarios for depicting the kinome-wide selectivity and the association with certain diseases. Overall, the computational model enables us to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning and is of practical value for exploring previously less studied kinases.
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