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 from DTIs networks, thereby achieving superior DTIs prediction performance. However, the majority of existing GCN-based methods utilize shallow graph neural networks, which are incapable of extracting higher-level semantic information. Additionally, the current training of models lacks an effective guiding mechanism, leading to the insufficient improvement of network's representation capabilities.

Results

In this paper, we propose a graph convolutional autoencoder model, named DDGAE, for D

SUBMITTER: Zeng M 

PROVIDER: S-EPMC12309189 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature