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
On-line přístup:	Získat plný text
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