GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network

Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based o...

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Published in:Frontiers in genetics Vol. 12; p. 650821
Main Authors: Liu, Zhixian, Chen, Qingfeng, Lan, Wei, Pan, Haiming, Hao, Xinkun, Pan, Shirui
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 09.04.2021
Subjects:
autoencoder
drug-target interaction prediction
Genetics
graph convolutional network
heterogeneous network
network embedding
random walk
graph convolutional network
random walk
drug-target interaction prediction
autoencoder
heterogeneous network
network embedding
ISSN:1664-8021, 1664-8021
Online Access:Get full text
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Summary:Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision–recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI.
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Edited by: Xiangxiang Zeng, Hunan University, China
This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
Reviewed by: Khanh N. Q. Le, Taipei Medical University, Taiwan; Yusen Zhang, Shandong University, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2021.650821