Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation

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....

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Xutong Li (7250720) (author)
مؤلفون آخرون:	Zhaojun Li (494105) (author), Xiaolong Wu (5008658) (author), Zhaoping Xiong (7178294) (author), Tianbiao Yang (7250723) (author), Zunyun Fu (7250726) (author), Xiaohong Liu (50816) (author), Xiaoqin Tan (7178291) (author), Feisheng Zhong (7250729) (author), Xiaozhe Wan (7250732) (author), Dingyan Wang (746691) (author), Xiaoyu Ding (391861) (author), Ruirui Yang (1678639) (author), Hui Hou (1547212) (author), Chunpu Li (1601443) (author), Hong Liu (50694) (author), Kaixian Chen (19449) (author), Hualiang Jiang (19452) (author), Mingyue Zheng (180908) (author)
منشور في:	2019
الموضوعات:	Genetics Biotechnology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified Experiment Validation model yields 1410 kinase-compound pairs novel chemical modulators bioactivity data 391 kinases drug repositioning kinome-wide selectivity drug discovery Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling prediction capability kinome interaction network activity data single-task models network algorithm Model Construction auROC
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Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation

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