An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing.

ABSTRACT: Drug repurposing is the process of discovering new indications (i.e., diseases or conditions) for already approved drugs. Many computational methods have been proposed for predicting new associations between drugs and diseases. In this article, we proposed a new method, called DR-HGNN, an integrative heterogeneous graph neural network-based method for multi-labeled drug repurposing, to discover new indications for existing drugs. For this purpose, we first used the DTINet dataset to construct a heterogeneous drug-protein-disease (DPD) network, which is a graph composed of four types of nodes (drugs, proteins, diseases, and drug side effects) and eight types of edges. Second, we labeled each drug-protein edge, dp _i,j = (d _i , p

SUBMITTER: Sadeghi S 

PROVIDER: S-EPMC9298882 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature