Prediction of drug–disease associations based on reinforcement symmetric metric learning and graph convolution network - Luo, Huimin; Zhu, Chunli; Wang, Jianlin; Zhang, Ge; Luo, Junwei; Yan, Chaokun (Frontiers in Pharmacology, 2024)
Accurately identifying novel indications for drugs is crucial in drug research and discovery. However, traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drugdisease prediction method based on metric learning algorithm has achieved not better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug-disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug-disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. The novel drug-disease associations based on the learned latent vector representation can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.