A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical...

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Autores principales:	Hua Yu, Jianxin Chen, Xue Xu, Yan Li, Huihui Zhao, Yupeng Fang, Xiuxiu Li, Wei Zhou, Wei Wang, Yonghua Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ea40cb89e25b401ab9039e278c05af95
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spelling	oai:doaj.org-article:ea40cb89e25b401ab9039e278c05af952021-11-18T07:16:58ZA systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.1932-620310.1371/journal.pone.0037608https://doaj.org/article/ea40cb89e25b401ab9039e278c05af952012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22666371/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.Hua YuJianxin ChenXue XuYan LiHuihui ZhaoYupeng FangXiuxiu LiWei ZhouWei WangYonghua WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 5, p e37608 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hua Yu Jianxin Chen Xue Xu Yan Li Huihui Zhao Yupeng Fang Xiuxiu Li Wei Zhou Wei Wang Yonghua Wang A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
description	In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.
format	article
author	Hua Yu Jianxin Chen Xue Xu Yan Li Huihui Zhao Yupeng Fang Xiuxiu Li Wei Zhou Wei Wang Yonghua Wang
author_facet	Hua Yu Jianxin Chen Xue Xu Yan Li Huihui Zhao Yupeng Fang Xiuxiu Li Wei Zhou Wei Wang Yonghua Wang
author_sort	Hua Yu
title	A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
title_short	A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
title_full	A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
title_fullStr	A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
title_full_unstemmed	A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
title_sort	systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/ea40cb89e25b401ab9039e278c05af95
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A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.

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