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In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion
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...
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Published in: | Journal of cheminformatics 2014-06, Vol.6 (1), Article 33 |
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container_title | Journal of cheminformatics |
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creator | Liu, Xian Xu, Yuan Li, Shanshan Wang, Yulan Peng, Jianlong Luo, Cheng Luo, Xiaomin Zheng, Mingyue Chen, Kaixian Jiang, Hualiang |
description | 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. |
doi_str_mv | 10.1186/1758-2946-6-33 |
format | article |
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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.</description><identifier>ISSN: 1758-2946</identifier><identifier>EISSN: 1758-2946</identifier><identifier>DOI: 10.1186/1758-2946-6-33</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Chemistry ; Chemistry and Materials Science ; Computational Biology/Bioinformatics ; Computer Applications in Chemistry ; Documentation and Information in Chemistry ; Research Article ; Theoretical and Computational Chemistry</subject><ispartof>Journal of cheminformatics, 2014-06, Vol.6 (1), Article 33</ispartof><rights>Liu et al.; licensee Chemistry Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1643-fbd71b363ba87cea9e9354be5b964945f378c6ba1fc8d7a486dc336d16b1deb33</citedby><cites>FETCH-LOGICAL-c1643-fbd71b363ba87cea9e9354be5b964945f378c6ba1fc8d7a486dc336d16b1deb33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Liu, Xian</creatorcontrib><creatorcontrib>Xu, Yuan</creatorcontrib><creatorcontrib>Li, Shanshan</creatorcontrib><creatorcontrib>Wang, Yulan</creatorcontrib><creatorcontrib>Peng, Jianlong</creatorcontrib><creatorcontrib>Luo, Cheng</creatorcontrib><creatorcontrib>Luo, Xiaomin</creatorcontrib><creatorcontrib>Zheng, Mingyue</creatorcontrib><creatorcontrib>Chen, Kaixian</creatorcontrib><creatorcontrib>Jiang, Hualiang</creatorcontrib><title>In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion</title><title>Journal of cheminformatics</title><addtitle>J Cheminform</addtitle><description>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.</description><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computer Applications in Chemistry</subject><subject>Documentation and Information in Chemistry</subject><subject>Research Article</subject><subject>Theoretical and Computational Chemistry</subject><issn>1758-2946</issn><issn>1758-2946</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp1kE1OwzAQRi0EEqWwZe0LpI1xYifsoFCoVIkFsLbGf6lL6hQ7FequB4HL9SQkKkJsWM2n0fdGo4fQJUlHhBRsTHheJFdlxhKWUHqEBr-L4z_5FJ3FuExTlvOUD9D7zONnVzvVtBAq02Lr4sL56hqD1sHE2GUMeL_7vHUVvoMW9rsvvA6NrM0Kyy2uXQVeJxKi0Ti6lashuHaLA_i3jo34w7ULrDsQ2010jT9HJxbqaC5-5hC9Tu9fJo_J_OlhNrmZJ4qwjCZWak4kZVRCwZWB0pQ0z6TJZcmyMsst5YViEohVheaQFUwrSpkmTBJtJKVDNDrcVaGJMRgr1sGtIGwFSUUvTPRORO9EMEF7YHwAYlf0lQli2WyC7378j_gGK9dwrA</recordid><startdate>20140618</startdate><enddate>20140618</enddate><creator>Liu, Xian</creator><creator>Xu, Yuan</creator><creator>Li, Shanshan</creator><creator>Wang, Yulan</creator><creator>Peng, Jianlong</creator><creator>Luo, Cheng</creator><creator>Luo, Xiaomin</creator><creator>Zheng, Mingyue</creator><creator>Chen, Kaixian</creator><creator>Jiang, Hualiang</creator><general>Springer International Publishing</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20140618</creationdate><title>In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion</title><author>Liu, Xian ; Xu, Yuan ; Li, Shanshan ; Wang, Yulan ; Peng, Jianlong ; Luo, Cheng ; Luo, Xiaomin ; Zheng, Mingyue ; Chen, Kaixian ; Jiang, Hualiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1643-fbd71b363ba87cea9e9354be5b964945f378c6ba1fc8d7a486dc336d16b1deb33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computer Applications in Chemistry</topic><topic>Documentation and Information in Chemistry</topic><topic>Research Article</topic><topic>Theoretical and Computational Chemistry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Xian</creatorcontrib><creatorcontrib>Xu, Yuan</creatorcontrib><creatorcontrib>Li, Shanshan</creatorcontrib><creatorcontrib>Wang, Yulan</creatorcontrib><creatorcontrib>Peng, Jianlong</creatorcontrib><creatorcontrib>Luo, Cheng</creatorcontrib><creatorcontrib>Luo, Xiaomin</creatorcontrib><creatorcontrib>Zheng, Mingyue</creatorcontrib><creatorcontrib>Chen, Kaixian</creatorcontrib><creatorcontrib>Jiang, Hualiang</creatorcontrib><collection>SpringerOpen</collection><collection>CrossRef</collection><jtitle>Journal of cheminformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Xian</au><au>Xu, Yuan</au><au>Li, Shanshan</au><au>Wang, Yulan</au><au>Peng, Jianlong</au><au>Luo, Cheng</au><au>Luo, Xiaomin</au><au>Zheng, Mingyue</au><au>Chen, Kaixian</au><au>Jiang, Hualiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion</atitle><jtitle>Journal of cheminformatics</jtitle><stitle>J Cheminform</stitle><date>2014-06-18</date><risdate>2014</risdate><volume>6</volume><issue>1</issue><artnum>33</artnum><issn>1758-2946</issn><eissn>1758-2946</eissn><abstract>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.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1186/1758-2946-6-33</doi><oa>free_for_read</oa></addata></record> |
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source | Open Access: PubMed Central; Full-Text Journals in Chemistry (Open access); Springer Nature - SpringerLink Journals - Fully Open Access ; Publicly Available Content (ProQuest) |
subjects | Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Documentation and Information in Chemistry Research Article Theoretical and Computational Chemistry |
title | In Silicotarget fishing: addressing a “Big Data” problem by ligand-based similarity rankings with data fusion |
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