The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, we add COVID-19-related information into our medical knowledge graph and utilize a knowledge-graph-based drug repositioning method to screen potential therapeutic drugs for COVID-19. Specific steps are as follows. Firstly, the information about COVID-19 is collected from the latest published literature, and gene targets of COVID-19 are added to the knowledge graph. Then, the information of COVID-19 of the knowledge graph is extracted and a drug–disease interaction prediction model based on Graph Convolutional Network with Attention (Att-GCN) is established. Att-GCN is used to extract features from the knowledge graph and the prediction matrix reconstructed through matrix operation. We evaluate the model by predicting drugs for both ordinary diseases and COVID-19. The model can achieve area under curve (AUC) of 0.954 and area under the precise recall area curve (AUPR) of 0.851 for ordinary diseases. On the drug repositioning experiment for COVID-19, five drugs predicted by the models have proved effective in clinical treatment. The experimental results confirm that the model can predict drug–disease interaction effectively for both normal diseases and COVID-19.

Background: Drug–target interaction (DTI) prediction is crucial in drug repositioning, as it can significantly reduce research and development costs and shorten the development cycle. Most existing deep learning–based approaches employ graph neural networks for DTI prediction. However, these approaches still face limitations in capturing complex biochemical features, integrating multilevel information, and providing interpretable model insights. Objective: This study proposes a heterogeneous network model based on multiview path aggregation, aiming to predict interactions between drugs and targets. Methods: This study employed a molecular attention transformer to extract 3D conformation features from the chemical structures of drugs and utilized Prot-T5, a protein-specific large language model, to deeply explore biophysically and functionally relevant features from protein sequences. By integrating drugs, proteins, diseases, and side effects from multisource heterogeneous data, we constructed a heterogeneous graph model to systematically characterize multidimensional associations between biological entities. On this foundation, a meta-path aggregation mechanism was proposed, which dynamically integrates information from both feature views and biological network relationship views. This mechanism effectively learned potential interaction patterns between biological entities and provided a more comprehensive representation of the complex relationships in the heterogeneous graph. It enhanced the model’s ability to capture sophisticated, context-dependent relationships in biological networks. Furthermore, we integrated multiscale features of drugs and proteins within the heterogeneous network, significantly improving the prediction accuracy of DTIs and enhancing the model’s interpretability and generalization ability. Results: In the DTI prediction task, the proposed model achieves an AUPR (area under the precision-recall curve) of 0.901 and an AUROC (area under the receiver operating characteristic curve) of 0.966, representing improvements of 1.7% and 0.8%, respectively, over the baseline methods. Furthermore, a case study on the KCNH2 target demonstrates that the proposed model successfully predicts 38 out of 53 candidate drugs as having interactions, which further validates its reliability and practicality in real-world scenarios. Conclusions: The proposed model shows marked superiority over baseline methods, highlighting the importance of integrating heterogeneous information with biological knowledge in DTI prediction.