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Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework
Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–targe...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881361/ https://www.ncbi.nlm.nih.gov/pubmed/20529913 http://dx.doi.org/10.1093/bioinformatics/btq176 |
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author | Yamanishi, Yoshihiro Kotera, Masaaki Kanehisa, Minoru Goto, Susumu |
author_facet | Yamanishi, Yoshihiro Kotera, Masaaki Kanehisa, Minoru Goto, Susumu |
author_sort | Yamanishi, Yoshihiro |
collection | PubMed |
description | Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug–target interaction networks, and show that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug–target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate compounds and in the integration of chemical, genomic and pharmacological data in a unified framework. In the results, we make predictions for four classes of important drug–target interactions involving enzymes, ion channels, GPCRs and nuclear receptors. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. Supplementary information: Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/pharmaco/. Availability: Softwares are available upon request. Contact: yoshihiro.yamanishi@ensmp.fr |
format | Text |
id | pubmed-2881361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-28813612010-06-08 Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework Yamanishi, Yoshihiro Kotera, Masaaki Kanehisa, Minoru Goto, Susumu Bioinformatics Ismb 2010 Conference Proceedings July 11 to July 13, 2010, Boston, Ma, Usa Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug–target interaction networks, and show that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug–target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate compounds and in the integration of chemical, genomic and pharmacological data in a unified framework. In the results, we make predictions for four classes of important drug–target interactions involving enzymes, ion channels, GPCRs and nuclear receptors. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. Supplementary information: Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/pharmaco/. Availability: Softwares are available upon request. Contact: yoshihiro.yamanishi@ensmp.fr Oxford University Press 2010-06-15 2010-06-01 /pmc/articles/PMC2881361/ /pubmed/20529913 http://dx.doi.org/10.1093/bioinformatics/btq176 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Ismb 2010 Conference Proceedings July 11 to July 13, 2010, Boston, Ma, Usa Yamanishi, Yoshihiro Kotera, Masaaki Kanehisa, Minoru Goto, Susumu Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework |
title | Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework |
title_full | Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework |
title_fullStr | Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework |
title_full_unstemmed | Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework |
title_short | Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework |
title_sort | drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework |
topic | Ismb 2010 Conference Proceedings July 11 to July 13, 2010, Boston, Ma, Usa |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881361/ https://www.ncbi.nlm.nih.gov/pubmed/20529913 http://dx.doi.org/10.1093/bioinformatics/btq176 |
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