Abstract:
: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.
journal_name
J Med Chemjournal_title
Journal of medicinal chemistryauthors
Li X,Li Z,Wu X,Xiong Z,Yang T,Fu Z,Liu X,Tan X,Zhong F,Wan X,Wang D,Ding X,Yang R,Hou H,Li C,Liu H,Chen K,Jiang H,Zheng Mdoi
10.1021/acs.jmedchem.9b00855subject
Has Abstractpub_date
2020-08-27 00:00:00pages
8723-8737issue
16eissn
0022-2623issn
1520-4804journal_volume
63pub_type
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