deepcpiadeeplearningbasedframeworkforlargescaleinsilicodrugscreening
文献类型：期刊论文
| 作者 | Wan Fangping5; Zhu Yue6; Hu Hailin1; Dai Antao6; Cai Xiaoqing6; Chen Ligong2; Gong Haipeng3; Xia Tian4; Yang Dehua6 ; Wang Mingwei6
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| 刊名 | genomicsproteomicsbioinformatics
 |
| 出版日期 | 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 |
| 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.School of Pharmaceutical Sciences,Tsinghua University 3.School of Life Science,Tsinghua University 4.Department of Electronics and Information Engineering,Huazhong University of Science and Technology 5.Institute for Interdisciplinary Information Sciences,Tsinghua University 6.中国科学院上海药物研究所 |
| 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. |
入库方式： OAI收割
来源：上海药物研究所
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