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

doi:10.1021/acs.jmedchem.9b00855

	Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation
	Li, Xutong 1,2; Li, Zhaojun 1,5; Wu, Xiaolong 1,4; Xiong, Zhaoping 1,3; Yang, Tianbiao 1,2; Fu, Zunyun 1,2; Liu, Xiaohong 1,3; Tan, Xiaoqin 1,2; Zhong, Feisheng 1,2; Wan, Xiaozhe 1,2
刊名	JOURNAL OF MEDICINAL CHEMISTRY
	2020-08-27
卷号	63 期号:16 页码:8723-8737
ISSN号	0022-2623
DOI	10.1021/acs.jmedchem.9b00855
通讯作者	Jiang, Hualiang(hljiang@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn)
英文摘要	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.
资助项目	National Natural Science Foundation of China[81773634] ; National Natural Science Foundation of China[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]
WOS关键词	TYROSINE KINASE INHIBITOR ; CELL LUNG-CANCER ; THERAPEUTIC TARGETS ; PROTEIN-KINASES ; NEURAL-NETWORKS ; PREDICTION ; GROWTH ; REGORAFENIB ; MULTICENTER ; K-115
WOS研究方向	Pharmacology & Pharmacy
语种	英语
出版者	AMER CHEMICAL SOC
WOS记录号	WOS:000566757500007
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/291070]
专题	中国科学院上海药物研究所
通讯作者	Jiang, Hualiang; Zheng, Mingyue
作者单位	1.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 3.ShanghaiTech Univ, Sch Life Sci & Technol, 393 Huaxiazhong Rd, Shanghai 200031, Peoples R China 4.East China Univ Sci & Technol, Sch Pharm, 130 Meilong Rd, Shanghai 200237, Peoples R China 5.Dezhou Univ, Sch Informat Management, 566 West Univ Rd, Dezhou 253023, Peoples R China 6.Fudan Univ, Sch Pharm, 826 Zhangheng Rd, Shanghai 201203, Peoples R China
推荐引用方式 GB/T 7714	Li, Xutong,Li, Zhaojun,Wu, Xiaolong,et al. Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation[J]. JOURNAL OF MEDICINAL CHEMISTRY,2020,63(16):8723-8737.
APA	Li, Xutong.,Li, Zhaojun.,Wu, Xiaolong.,Xiong, Zhaoping.,Yang, Tianbiao.,...&Zheng, Mingyue.(2020).Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation.JOURNAL OF MEDICINAL CHEMISTRY,63(16),8723-8737.
MLA	Li, Xutong,et al."Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation".JOURNAL OF MEDICINAL CHEMISTRY 63.16(2020):8723-8737.