In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion
文献类型：期刊论文
| 作者 | Liu,Xian2; Xu,Yuan2; Li,Shanshan2; Wang,Yulan2; Peng,Jianlong2; Luo,Cheng2 ; Luo,Xiaomin2; Zheng,Mingyue2; Chen,Kaixian1,2; Jiang,Hualiang1,2
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| 刊名 | Journal of Cheminformatics
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| 出版日期 | 2014-06-18 |
| 卷号 | 6期号:1 |
| 关键词 | Target fishing Big data Molecular fingerprints Data fusion Similarity searching |
| ISSN号 | 1758-2946 |
| DOI | 10.1186/1758-2946-6-33 |
| 通讯作者 | Luo,Xiaomin(xmluo@mail.shcnc.ac.cn) ; Zheng,Mingyue(myzheng@mail.shcnc.ac.cn) |
| 英文摘要 | AbstractBackgroundLigand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound.ResultsWe tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities.ConclusionsWith the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery. |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/1758-2946-6-33 |
| 出版者 | Springer International Publishing |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/307075]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Luo,Xiaomin; Zheng,Mingyue |
| 作者单位 | 1.School of Life Science and Technology, ShanghaiTech University 2.Shanghai Institute of Materia Medica, Chinese Academy of Sciences; Drug Discovery and Design Center, State Key Laboratory of Drug Research |
| 推荐引用方式 GB/T 7714 | Liu,Xian,Xu,Yuan,Li,Shanshan,et al. In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion[J]. Journal of Cheminformatics,2014,6(1). |
| APA | Liu,Xian.,Xu,Yuan.,Li,Shanshan.,Wang,Yulan.,Peng,Jianlong.,...&Jiang,Hualiang.(2014).In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion.Journal of Cheminformatics,6(1). |
| MLA | Liu,Xian,et al."In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion".Journal of Cheminformatics 6.1(2014). |
入库方式： OAI收割
来源：上海药物研究所
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