GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction

Abstract Motivation Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug–protein interactions mainly focus on exploiting various data about drugs and pr...

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Vydáno v:	Briefings in bioinformatics Ročník 23; číslo 1
Hlavní autoři:	Xuan, Ping, Fan, Mengsi, Cui, Hui, Zhang, Tiangang, Nakaguchi, Toshiya
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Oxford University Press 17.01.2022 Oxford Publishing Limited (England)
Témata:	Artificial neural networks Bioinformatics Contact potentials Drug development Drug Interactions Drugs Embedding Information processing Multilayers Neural networks Neural Networks, Computer Nodes Predictions Protein interaction Protein structure Proteins Representations Topology Drug–protein interaction prediction Attention-enhanced topological representation Pairwise attribute representation Graph convolutional and variational autoencoders Pairwise attribute distribution
ISSN:	1467-5463, 1477-4054, 1477-4054
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Shrnutí:	Abstract Motivation Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug–protein interactions mainly focus on exploiting various data about drugs and proteins. These methods failed to completely learn and integrate the attribute information of a pair of drug and protein nodes and their attribute distribution. Results We present a new prediction method, GVDTI, to encode multiple pairwise representations, including attention-enhanced topological representation, attribute representation and attribute distribution. First, a framework based on graph convolutional autoencoder is constructed to learn attention-enhanced topological embedding that integrates the topology structure of a drug–protein network for each drug and protein nodes. The topological embeddings of each drug and each protein are then combined and fused by multi-layer convolution neural networks to obtain the pairwise topological representation, which reveals the hidden topological relationships between drug and protein nodes. The proposed attribute-wise attention mechanism learns and adjusts the importance of individual attribute in each topological embedding of drug and protein nodes. Secondly, a tri-layer heterogeneous network composed of drug, protein and disease nodes is created to associate the similarities, interactions and associations across the heterogeneous nodes. The attribute distribution of the drug–protein node pair is encoded by a variational autoencoder. The pairwise attribute representation is learned via a multi-layer convolutional neural network to deeply integrate the attributes of drug and protein nodes. Finally, the three pairwise representations are fused by convolutional and fully connected neural networks for drug–protein interaction prediction. The experimental results show that GVDTI outperformed other seven state-of-the-art methods in comparison. The improved recall rates indicate that GVDTI retrieved more actual drug–protein interactions in the top ranked candidates than conventional methods. Case studies on five drugs further confirm GVDTI’s ability in discovering the potential candidate drug-related proteins. Contact zhang@hlju.edu.cn Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1467-5463 1477-4054 1477-4054
DOI:	10.1093/bib/bbab453