Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN

Abstract Background The exploration of drug-target interactions (DTIs) is a critical step in drug discovery and drug repurposing. Recently, network-based methods have emerged as a prominent research area for predicting DTIs. These methods excel by extracting both topological and feature information...

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Bibliographic Details
Main Authors: Ming Zeng, Min Wang, Fuqiang Xie, Zhiwei Ji
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Bioinformatics
Subjects:
Drug-target interaction
Dynamic weighting convolutional residual connection
Dual self-supervised joint training mechanism
Graph convolutional autoencoder
Generative adversarial network
Online Access:https://doi.org/10.1186/s12859-025-06198-x
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https://doi.org/10.1186/s12859-025-06198-x

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