Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks

An important stage in the process of discovering new drugs is when candidate molecules are tested of their efficacy. It is reported that testing drug efficacy empirically costs billions of dollars in the drug discovery pipeline. As a mechanism of expediting this process, researchers have resorted to...

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Autores principales:	Mapopa Chipofya, Hilal Tayara, Kil To Chong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	medical subheading drug function drug repurposing graph convolutional networks Pharmacy and materia medica RS1-441
Acceso en línea:	https://doaj.org/article/b9e654e2853f4a34ac322b55a62ef5c2
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id	oai:doaj.org-article:b9e654e2853f4a34ac322b55a62ef5c2
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spelling	oai:doaj.org-article:b9e654e2853f4a34ac322b55a62ef5c22021-11-25T18:41:41ZDrug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks10.3390/pharmaceutics131119061999-4923https://doaj.org/article/b9e654e2853f4a34ac322b55a62ef5c22021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4923/13/11/1906https://doaj.org/toc/1999-4923An important stage in the process of discovering new drugs is when candidate molecules are tested of their efficacy. It is reported that testing drug efficacy empirically costs billions of dollars in the drug discovery pipeline. As a mechanism of expediting this process, researchers have resorted to using computational methods to predict the action of molecules in silico. Here, we present a way of predicting the therapeutic-use class of drugs from chemical structures only using graph convolutional networks. In comparison with existing methods which use fingerprints or images as training samples, our approach has yielded better results in all metrics under consideration. In particular, validation accuracy increased from 83–88% to 86–90% for single label tasks. Similarly, the model achieved an accuracy of over 88% on new test data. Finally, our multi-label classification model made new predictions which indicated that some of the drugs could have other therapeutic uses other than those indicated in the dataset. We performed a literature-based evaluation of these predictions and found evidence that validates them. This renders the model a potential tool to be used in search of drugs that are candidates for repurposing.Mapopa ChipofyaHilal TayaraKil To ChongMDPI AGarticlemedical subheadingdrug functiondrug repurposinggraph convolutional networksPharmacy and materia medicaRS1-441ENPharmaceutics, Vol 13, Iss 1906, p 1906 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	medical subheading drug function drug repurposing graph convolutional networks Pharmacy and materia medica RS1-441
spellingShingle	medical subheading drug function drug repurposing graph convolutional networks Pharmacy and materia medica RS1-441 Mapopa Chipofya Hilal Tayara Kil To Chong Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks
description	An important stage in the process of discovering new drugs is when candidate molecules are tested of their efficacy. It is reported that testing drug efficacy empirically costs billions of dollars in the drug discovery pipeline. As a mechanism of expediting this process, researchers have resorted to using computational methods to predict the action of molecules in silico. Here, we present a way of predicting the therapeutic-use class of drugs from chemical structures only using graph convolutional networks. In comparison with existing methods which use fingerprints or images as training samples, our approach has yielded better results in all metrics under consideration. In particular, validation accuracy increased from 83–88% to 86–90% for single label tasks. Similarly, the model achieved an accuracy of over 88% on new test data. Finally, our multi-label classification model made new predictions which indicated that some of the drugs could have other therapeutic uses other than those indicated in the dataset. We performed a literature-based evaluation of these predictions and found evidence that validates them. This renders the model a potential tool to be used in search of drugs that are candidates for repurposing.
format	article
author	Mapopa Chipofya Hilal Tayara Kil To Chong
author_facet	Mapopa Chipofya Hilal Tayara Kil To Chong
author_sort	Mapopa Chipofya
title	Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks
title_short	Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks
title_full	Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks
title_fullStr	Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks
title_full_unstemmed	Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks
title_sort	drug therapeutic-use class prediction and repurposing using graph convolutional networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b9e654e2853f4a34ac322b55a62ef5c2
work_keys_str_mv	AT mapopachipofya drugtherapeuticuseclasspredictionandrepurposingusinggraphconvolutionalnetworks AT hilaltayara drugtherapeuticuseclasspredictionandrepurposingusinggraphconvolutionalnetworks AT kiltochong drugtherapeuticuseclasspredictionandrepurposingusinggraphconvolutionalnetworks
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Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks

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