In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion

doi:10.1186/1758-2946-6-33

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	In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion
	Liu, Xian 1; Xu, Yuan 1; Li, Shanshan 1; Wang, Yulan 1; Peng, Jianlong 1; Luo, Cheng1 ; Luo, Xiaomin1 ; Zheng, Mingyue1 ; Chen, Kaixian1,2 ; Jiang, Hualiang1,2
刊名	JOURNAL OF CHEMINFORMATICS
	2014-06-18
卷号	6
关键词	Target fishing Big data Molecular fingerprints Data fusion Similarity searching
ISSN号	1758-2946
DOI	10.1186/1758-2946-6-33
文献子类	Article
英文摘要	Background: Ligand-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. Results: We 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. Conclusions: With 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.
资助项目	Hi-Tech Research and Development Program of China[2012AA020302] ; National Science and Technology Major Project "Key New Drug Creation and Manufacturing Program"[2013ZX09507001] ; National Science and Technology Major Project "Key New Drug Creation and Manufacturing Program"[2014ZX09507002] ; National Natural Science Foundation of China[81230076] ; National Natural Science Foundation of China[21210003]
WOS关键词	CHEMICAL SIMILARITY ; PROTEIN DATABASE ; DRUG DISCOVERY ; POLYPHARMACOLOGY ; SEARCH ; IDENTIFICATION ; PHARMACOLOGY ; VALIDATION ; ROPINIROLE ; PREDICTION
WOS研究方向	Chemistry ; Computer Science
语种	英语
出版者	BIOMED CENTRAL LTD
WOS记录号	WOS:000338586500001
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/277020]
专题	药物发现与设计中心中科院受体结构与功能重点实验室新药研究国家重点实验室
通讯作者	Luo, Xiaomin
作者单位	1.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & Design Ctr, Shanghai 201203, Peoples R China; 2.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xian,Xu, Yuan,Li, Shanshan,et al. In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion[J]. JOURNAL OF CHEMINFORMATICS,2014,6.
APA	Liu, Xian.,Xu, Yuan.,Li, Shanshan.,Wang, Yulan.,Peng, Jianlong.,...&Jiang, Hualiang.(2014).In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion.JOURNAL OF CHEMINFORMATICS,6.
MLA	Liu, Xian,et al."In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion".JOURNAL OF CHEMINFORMATICS 6(2014).