Link Prediction based on Tensor Decomposition for the Knowledge Graph of COVID-19 Antiviral Drug

Due to the large-scale spread of COVID-19, which has a significant impact on human health and social economy, developing effective antiviral drugs for COVID-19 is vital to saving human lives. Various biomedical associations, e.g., drug-virus and viral protein-host protein interactions, can be used for building biomedical knowledge graphs. Based on these sources, large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses. To utilize the various heterogeneous biomedical associations, we proposed a fusion strategy to integrate the results of two tensor decomposition-based models（i.e., CP-N3 and Compl Ex-N3）. Sufficient experiments indicated that our method obtained high performance（MRR=0.2328）. Compared with CP-N3, the mean reciprocal rank（MRR） is increased by 3.3% and compared with Compl Ex-N3, the MRR is increased by 3.5%. Meanwhile, we explored the relationship between the performance and relationship types, which indicated that there is a negative correlation（PCC=0.446, P-value=2.26 e-194） between the performance of triples predicted by our method and edge betweenness.