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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| Veröffentlicht in: | Frontiers in pharmacology Jg. 13; S. 1056605 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Lei, Song Liu, Lian Lei, Xiujuan |
| AuthorAffiliation | School of Computer Science , Shaanxi Normal University , Xi’an , China |
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| Cites_doi | 10.1016/j.artmed.2014.11.003 10.1039/c2mb00002d 10.3389/fphar.2022.872785 10.1093/infdis/jix186 10.1007/s15010-020-01438-z 10.1186/s12859-018-2220-4 10.1093/bioinformatics/btaa157 10.1016/j.ijid.2020.10.069 10.1186/s12859-020-3379-z 10.1093/bib/bbz176 10.1093/bioinformatics/btq241 10.1093/bib/bbaa025 10.1093/bioinformatics/bty013 10.1093/bioinformatics/btw228 10.1186/1758-2946-5-30 10.1093/bioinformatics/btz182 10.1166/jnn.2020.18955 10.1021/acsami.1c02755 10.1007/3-540-44886-1_25 10.1093/bioinformatics/btu403 10.4103/0022-3859.81870 10.1093/nar/gku1243 10.3390/ijms20174102 10.1093/bib/bbt056 10.1371/journal.pone.0118432 10.7150/ijbs.9224 10.1007/s11030-022-10492-8 10.1038/534314a 10.1093/bioinformatics/btz418 10.1186/s12859-021-04127-2 10.1038/nrd.2018.168 10.1038/nrd1468 10.1089/cmb.2010.0213 10.1093/bioinformatics/btz331 10.1038/msb.2011.26 10.1093/nar/gky092 10.1080/1062936X.2016.1209241 10.1186/s12879-020-05698-w 10.1186/s12879-021-06164-x 10.1377/hlthaff.25.2.420 10.1093/nar/gkv951 10.1038/nrd1382 10.1155/2015/275045 10.2169/internalmedicine.7971-21 10.1093/bib/bbaa104 10.1093/bioinformatics/btaa010 10.1093/bib/bbaa141 10.1109/JBHI.2020.3039502 10.1093/bioinformatics/btw770 10.1093/bib/bbaa243 |
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| Keywords | COVID-19 graph representation learning drug repositioning heterogeneous network variational graph autoencoders |
| Language | English |
| License | Copyright © 2022 Lei, Lei and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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