Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other word...

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Vydané v:	BMC bioinformatics Ročník 24; číslo 1; s. 151 - 21
Hlavní autori:	Chen, Peng, Zheng, Haoran
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 17.04.2023 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Analysis Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Datasets Deep learning Drug Development - methods Drug discovery Drug Discovery - methods Drug interactions Drug-target interaction Drugs Embedding Graph convolutional autoencoder Graphical representations Invariance Life Sciences Ligands Machine learning Medical care, Cost of Methods Microarrays Neural networks Nodes Polypharmacy Predictions Proteins Spatial consistency constraint China Deep learning Spatial consistency constraint Graph convolutional autoencoder Drug-target interaction
ISSN:	1471-2105, 1471-2105
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Shrnutí:	Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other words, they do not take into account the invariance of the topological relationships between nodes during representation learning. It may limit the performance of the DTI prediction methods. Results Here, we propose a novel graph convolutional autoencoder-based model, named SDGAE, to predict DTIs. As the graph convolutional network cannot handle isolated nodes in a network, a pre-processing step was applied to reduce the number of isolated nodes in the heterogeneous network and facilitate effective exploitation of the graph convolutional network. By maintaining the graph structure during representation learning, the nearest neighbour relationships between nodes in the embedding space remained as close as possible to the original space. Conclusions Overall, we demonstrated that SDGAE can automatically learn more informative and robust feature vectors of drugs and targets, thus exhibiting significantly improved predictive accuracy for DTIs.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-023-05275-3