Predicting drug-target interaction networks based on functional groups and biological features.

<h4>Background</h4>Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction m...

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Autores principales:	Zhisong He, Jian Zhang, Xiao-He Shi, Le-Le Hu, Xiangyin Kong, Yu-Dong Cai, Kuo-Chen Chou
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/22e8897d62774f45b1e66542934054cf
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spelling	oai:doaj.org-article:22e8897d62774f45b1e66542934054cf2021-11-25T06:25:31ZPredicting drug-target interaction networks based on functional groups and biological features.1932-620310.1371/journal.pone.0009603https://doaj.org/article/22e8897d62774f45b1e66542934054cf2010-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20300175/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.<h4>Methods/principal findings</h4>To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.<h4>Conclusion/significance</h4>Our results indicate that the network prediction system thus established is quite promising and encouraging.Zhisong HeJian ZhangXiao-He ShiLe-Le HuXiangyin KongYu-Dong CaiKuo-Chen ChouPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 3, p e9603 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhisong He Jian Zhang Xiao-He Shi Le-Le Hu Xiangyin Kong Yu-Dong Cai Kuo-Chen Chou Predicting drug-target interaction networks based on functional groups and biological features.
description	<h4>Background</h4>Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner.<h4>Methods/principal findings</h4>To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively.<h4>Conclusion/significance</h4>Our results indicate that the network prediction system thus established is quite promising and encouraging.
format	article
author	Zhisong He Jian Zhang Xiao-He Shi Le-Le Hu Xiangyin Kong Yu-Dong Cai Kuo-Chen Chou
author_facet	Zhisong He Jian Zhang Xiao-He Shi Le-Le Hu Xiangyin Kong Yu-Dong Cai Kuo-Chen Chou
author_sort	Zhisong He
title	Predicting drug-target interaction networks based on functional groups and biological features.
title_short	Predicting drug-target interaction networks based on functional groups and biological features.
title_full	Predicting drug-target interaction networks based on functional groups and biological features.
title_fullStr	Predicting drug-target interaction networks based on functional groups and biological features.
title_full_unstemmed	Predicting drug-target interaction networks based on functional groups and biological features.
title_sort	predicting drug-target interaction networks based on functional groups and biological features.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/22e8897d62774f45b1e66542934054cf
work_keys_str_mv	AT zhisonghe predictingdrugtargetinteractionnetworksbasedonfunctionalgroupsandbiologicalfeatures AT jianzhang predictingdrugtargetinteractionnetworksbasedonfunctionalgroupsandbiologicalfeatures AT xiaoheshi predictingdrugtargetinteractionnetworksbasedonfunctionalgroupsandbiologicalfeatures AT lelehu predictingdrugtargetinteractionnetworksbasedonfunctionalgroupsandbiologicalfeatures AT xiangyinkong predictingdrugtargetinteractionnetworksbasedonfunctionalgroupsandbiologicalfeatures AT yudongcai predictingdrugtargetinteractionnetworksbasedonfunctionalgroupsandbiologicalfeatures AT kuochenchou predictingdrugtargetinteractionnetworksbasedonfunctionalgroupsandbiologicalfeatures
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Predicting drug-target interaction networks based on functional groups and biological features.

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