An Integrative Heterogeneous Graph Neural Network–Based Method for Multi-Labeled Drug Repurposing - Sadeghi, Shaghayegh; Lu , Jianguo; Ngom , Alioune (Frontiers in Pharmacology, 2022)
Drug repurposing is the process of discovering new indications (ie, 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 propose 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 use 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 label each drug-protein edge, dp_ij=(d_i, p_j), of the DPD network with a set of diseases, {δ_{i,j,1}, ..., δ_{i,j,k}} associated with both d_i and p_j and then devise a multi-label ranking approaches which incorporate a neural network architecture that operates on the heterogeneous graph-structured data, and which leverages both the interaction patterns and the features of drug and protein nodes. We use a derivative of the GraphSAGE algorithm, HinSAGE, on the heterogeneous DPD network to learn low-dimensional vector representation of features of drugs and proteins. Finally, we use the drug-protein network to learn the embeddings of the drug-protein edges and then predict the disease labels that act as bridges between drugs and proteins. The proposed method shows better results when compared to existing methods applied to the DTINet dataset, with an AUC of 0.964.