NeuRank: learning to rank with neural networks for drug–target interaction prediction

Abstract Background Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug–target interactions (DTIs) has intensified. Results We treat the prediction of DTIs as a ranking problem and pr...

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Autores principales:	Xiujin Wu, Wenhua Zeng, Fan Lin, Xiuze Zhou
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Drug–target interactions Drug discovery Neural network Ranking task Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/45ca5360d403433b8f68378e784b6a64
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spelling	oai:doaj.org-article:45ca5360d403433b8f68378e784b6a642021-11-28T12:11:13ZNeuRank: learning to rank with neural networks for drug–target interaction prediction10.1186/s12859-021-04476-y1471-2105https://doaj.org/article/45ca5360d403433b8f68378e784b6a642021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04476-yhttps://doaj.org/toc/1471-2105Abstract Background Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug–target interactions (DTIs) has intensified. Results We treat the prediction of DTIs as a ranking problem and propose a neural network architecture, NeuRank, to address it. Also, we assume that similar drug compounds are likely to interact with similar target proteins. Thus, in our model, we add drug and target similarities, which are very effective at improving the prediction of DTIs. Then, we develop NeuRank from a point-wise to a pair-wise, and further to list-wise model. Conclusion Finally, results from extensive experiments on five public data sets (DrugBank, Enzymes, Ion Channels, G-Protein-Coupled Receptors, and Nuclear Receptors) show that, in identifying DTIs, our models achieve better performance than other state-of-the-art methods.Xiujin WuWenhua ZengFan LinXiuze ZhouBMCarticleDrug–target interactionsDrug discoveryNeural networkRanking taskComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Drug–target interactions Drug discovery Neural network Ranking task Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Drug–target interactions Drug discovery Neural network Ranking task Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Xiujin Wu Wenhua Zeng Fan Lin Xiuze Zhou NeuRank: learning to rank with neural networks for drug–target interaction prediction
description	Abstract Background Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug–target interactions (DTIs) has intensified. Results We treat the prediction of DTIs as a ranking problem and propose a neural network architecture, NeuRank, to address it. Also, we assume that similar drug compounds are likely to interact with similar target proteins. Thus, in our model, we add drug and target similarities, which are very effective at improving the prediction of DTIs. Then, we develop NeuRank from a point-wise to a pair-wise, and further to list-wise model. Conclusion Finally, results from extensive experiments on five public data sets (DrugBank, Enzymes, Ion Channels, G-Protein-Coupled Receptors, and Nuclear Receptors) show that, in identifying DTIs, our models achieve better performance than other state-of-the-art methods.
format	article
author	Xiujin Wu Wenhua Zeng Fan Lin Xiuze Zhou
author_facet	Xiujin Wu Wenhua Zeng Fan Lin Xiuze Zhou
author_sort	Xiujin Wu
title	NeuRank: learning to rank with neural networks for drug–target interaction prediction
title_short	NeuRank: learning to rank with neural networks for drug–target interaction prediction
title_full	NeuRank: learning to rank with neural networks for drug–target interaction prediction
title_fullStr	NeuRank: learning to rank with neural networks for drug–target interaction prediction
title_full_unstemmed	NeuRank: learning to rank with neural networks for drug–target interaction prediction
title_sort	neurank: learning to rank with neural networks for drug–target interaction prediction
publisher	BMC
publishDate	2021
url	https://doaj.org/article/45ca5360d403433b8f68378e784b6a64
work_keys_str_mv	AT xiujinwu neuranklearningtorankwithneuralnetworksfordrugtargetinteractionprediction AT wenhuazeng neuranklearningtorankwithneuralnetworksfordrugtargetinteractionprediction AT fanlin neuranklearningtorankwithneuralnetworksfordrugtargetinteractionprediction AT xiuzezhou neuranklearningtorankwithneuralnetworksfordrugtargetinteractionprediction
_version_	1718408135734984704

NeuRank: learning to rank with neural networks for drug–target interaction prediction

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