deepcpiadeeplearningbasedframeworkforlargescaleinsilicodrugscreening

	deepcpiadeeplearningbasedframeworkforlargescaleinsilicodrugscreening
	Wan Fangping 2; Zhu Yue 3; Hu Hailin 1; Dai Antao 3; Cai Xiaoqing 3; Chen Ligong 4; Gong Haipeng 5; Xia Tian 6; Yang Dehua 3; Wang Mingwei 3
刊名	genomicsproteomicsbioinformatics
	2019
卷号	17 期号:5 页码:478
关键词	Deep learning Machine learning Drug discovery Compound–protein interaction prediction
ISSN号	1672-0229
英文摘要	Accurate identification of compound–protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug–target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https: .
语种	英语
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/280691]
专题	中国科学院上海药物研究所
作者单位	1.School of Medicine,Tsinghua University 2.Institute for Interdisciplinary Information Sciences,Tsinghua University 3.中国科学院上海药物研究所 4.School of Pharmaceutical Sciences,Tsinghua University 5.School of Life Science,Tsinghua University 6.Department of Electronics and Information Engineering,Huazhong University of Science and Technology
推荐引用方式 GB/T 7714	Wan Fangping,Zhu Yue,Hu Hailin,et al. deepcpiadeeplearningbasedframeworkforlargescaleinsilicodrugscreening[J]. genomicsproteomicsbioinformatics,2019,17(5):478.
APA	Wan Fangping.,Zhu Yue.,Hu Hailin.,Dai Antao.,Cai Xiaoqing.,...&Zeng Jianyang.(2019).deepcpiadeeplearningbasedframeworkforlargescaleinsilicodrugscreening.genomicsproteomicsbioinformatics,17(5),478.
MLA	Wan Fangping,et al."deepcpiadeeplearningbasedframeworkforlargescaleinsilicodrugscreening".genomicsproteomicsbioinformatics 17.5(2019):478.