Prediction of drug-target interactions and drug repositioning via network-based inference.

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods w...

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Autores principales:	Feixiong Cheng, Chuang Liu, Jing Jiang, Weiqiang Lu, Weihua Li, Guixia Liu, Weixing Zhou, Jin Huang, Yun Tang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/44f9470ce36d4f04869bb915c3910da5
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spelling	oai:doaj.org-article:44f9470ce36d4f04869bb915c3910da52021-11-18T05:51:21ZPrediction of drug-target interactions and drug repositioning via network-based inference.1553-734X1553-735810.1371/journal.pcbi.1002503https://doaj.org/article/44f9470ce36d4f04869bb915c3910da52012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22589709/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.Feixiong ChengChuang LiuJing JiangWeiqiang LuWeihua LiGuixia LiuWeixing ZhouJin HuangYun TangPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 5, p e1002503 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Feixiong Cheng Chuang Liu Jing Jiang Weiqiang Lu Weihua Li Guixia Liu Weixing Zhou Jin Huang Yun Tang Prediction of drug-target interactions and drug repositioning via network-based inference.
description	Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.
format	article
author	Feixiong Cheng Chuang Liu Jing Jiang Weiqiang Lu Weihua Li Guixia Liu Weixing Zhou Jin Huang Yun Tang
author_facet	Feixiong Cheng Chuang Liu Jing Jiang Weiqiang Lu Weihua Li Guixia Liu Weixing Zhou Jin Huang Yun Tang
author_sort	Feixiong Cheng
title	Prediction of drug-target interactions and drug repositioning via network-based inference.
title_short	Prediction of drug-target interactions and drug repositioning via network-based inference.
title_full	Prediction of drug-target interactions and drug repositioning via network-based inference.
title_fullStr	Prediction of drug-target interactions and drug repositioning via network-based inference.
title_full_unstemmed	Prediction of drug-target interactions and drug repositioning via network-based inference.
title_sort	prediction of drug-target interactions and drug repositioning via network-based inference.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/44f9470ce36d4f04869bb915c3910da5
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Prediction of drug-target interactions and drug repositioning via network-based inference.

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