Graph convolution network based on meta-paths and mutual information for drug-target interaction prediction.

ABSTRACT:

Background

Predicting drug-target interactions (DTIs) plays a pivotal role in accelerating drug repositioning by prioritizing candidate drugs and reducing experimental costs. Despite advancements in deep learning, several challenges still require further exploration, including sparsity and inadequate representation of feature relationships.

Results

We propose GCNMM, a novel graph convolutional network based on meta-paths and mutual information, to predict latent DTIs in drug-target heterogeneous networks. Our approach begins by constructing a fused DTI network based on meta-paths and a graph attention network. We compute multiple similarity networks by using Jaccard coefficients and integrate them into the fused drug and target similarity networks through entropy-based fusio

SUBMITTER: Cao S 

PROVIDER: S-EPMC12595897 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature