Drug repositioning based on heterogeneous networks and variational graph autoencoders

Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly. The emergence of more and more artificial intelligence-based drug repositioning metho...

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Published in:Frontiers in pharmacology Vol. 13; p. 1056605
Main Authors: Lei, Song, Lei, Xiujuan, Liu, Lian
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 21.12.2022
Subjects:
COVID-19
drug repositioning
graph representation learning
heterogeneous network
Pharmacology
variational graph autoencoders
COVID-19
graph representation learning
drug repositioning
heterogeneous network
variational graph autoencoders
ISSN:1663-9812, 1663-9812
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Summary:Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly. The emergence of more and more artificial intelligence-based drug repositioning methods in the past 2 years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder. First, a drug-disease heterogeneous network is established based on three drug attributes, disease semantic information, and known drug-disease associations. Second, low-dimensional feature representations for heterogeneous networks are learned through a variational graph autoencoder module and a multi-layer convolutional module. Finally, the feature representation is fed to a fully connected layer and a Softmax layer to predict new drug-disease associations. Comparative experiments with other baseline methods on three datasets demonstrate the excellent performance of VGAEDR. In the case study, we predicted the top 10 possible anti-COVID-19 drugs on the existing drug and disease data, and six of them were verified by other literatures.
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Seyedehzahra Sajadi, Yazd University, Iran
Edited by: Sajjad Gharaghani, University of Tehran, Iran
This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology
Reviewed by: Luciana Scotti, Federal University of Paraíba, Brazil
ISSN:1663-9812
1663-9812
DOI:10.3389/fphar.2022.1056605