An Enhanced LRMC Method for Drug Repositioning via GCN-based HIN Embedding

Drug repositioning has received ever-increasing attention in the field of drug discovery over the last few years. However, the high efficient prediction methods taking full advantage of heterogeneous information networks (HINs) still deserves further research. To this end, this paper proposes an approach for drug repositioning via integrating HINs embedding and link prediction for more potential drug-target interactions. To utilize multiple side information, we introduce a graph convolutional network (GCN) based embedding method for HINs. The obtained drug-related and target-related information is adopted to improve the low-rank matrix completion (LRMC) model. Moreover, a regulation for alleviating the noise of negative samples is designed to enhance the optimization of LRMC. The experiments conducted on the comparative database demonstrate that the proposed method is more effective than the existing approaches in the prediction of drug repositioning.