Prediction of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections
文献类型：期刊论文
| 作者 | Chen, Lei1; Lu, Jing2; Luo, Xiaomin2 ; Feng, Kai-Yan3
|
| 刊名 | BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS
 |
| 出版日期 | 2014-01 |
| 卷号 | 1844期号:1页码:207-213 |
| 关键词 | Drug-target interaction network Chemical-chemical similarity Chemical-chemical connection Chemical-protein connection Jackknife test |
| ISSN号 | 1570-9639 |
| DOI | 10.1016/j.bbapap.2013.05.021 |
| 文献子类 | Article |
| 英文摘要 | Drug-target interaction is a key research topic in drug discovery since correct identification of target proteins of drug candidates can help screen out those with unacceptable toxicities, thereby saving expense. In this study, we developed a novel computational approach to predict drug target groups that may reduce the number of candidate target proteins associated with a query drug. A benchmark dataset, consisting of 3028 drugs assigned within nine categories, was constructed by collecting data from KEGG. The nine categories are (1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens. The proposed method combines the data gleaned from chemical-chemical similarities, chemical-chemical connections and chemical-protein connections to allocate drugs to each of the nine target groups. A jackknife test applied to the training dataset that was constructed from the benchmark dataset, provided an overall correct prediction rate of 87.45%, as compared to 87.79% for the test dataset that was constructed by randomly selecting 10% of samples from the benchmark dataset. These prediction rates are much higher than the 11.11% achieved by random guesswork. These promising results suggest that the proposed method can become a useful tool in identifying drug target groups. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai. (C) 2013 Elsevier B.V. All rights reserved. |
| WOS关键词 | FUNCTIONAL DOMAIN COMPOSITION ; INTERACTION NETWORKS ; COMPOUND SIMILARITY ; BINDING PROTEINS ; SMALL MOLECULES ; MODEL ; INFORMATION ; INHIBITORS ; ONTOLOGY ; DOCKING |
| 资助项目 | National Natural Science Foundation of China[61202021] ; National Natural Science Foundation of China[61105097] ; Shanghai Educational Development Foundation[12CG55] ; Innovation Program of Shanghai Municipal Education Commission[12YZ120] ; Science AMP[00000000] ; Technology Program of Shanghai Maritime University[20120105] ; Hi-TECH Research and Development Program of China[2012AA020308] ; National ST Major Project[2012ZX09301-001-002] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:000330911500007 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/277306]  |
| 专题 | 药物发现与设计中心 药物安全性评价中心 |
| 通讯作者 | Feng, Kai-Yan |
| 作者单位 | 1.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; 2.Shanghai Inst Mat Med, DDDC, Shanghai 201203, Peoples R China; 3.BGI Shenzhen, Beishan Ind Zone, Shenzhen 518083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Lei,Lu, Jing,Luo, Xiaomin,et al. Prediction of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections[J]. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS,2014,1844(1):207-213. |
| APA | Chen, Lei,Lu, Jing,Luo, Xiaomin,&Feng, Kai-Yan.(2014).Prediction of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections.BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS,1844(1),207-213. |
| MLA | Chen, Lei,et al."Prediction of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections".BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 1844.1(2014):207-213. |
入库方式： OAI收割
来源：上海药物研究所
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