## 5 Experiments
### DDI Prediction for Other Diseases  
At present, only a small number of medicines have proven to have a therapeutic effect on COVID-19. Therefore, only performing DDI experiments on COVID-19 cannot fully verify the effectiveness of the Att-GCN-DDI model Therefore, we also test our model on DDI prediction for other diseases.  
The DDI prediction is a binary classification problem, in which known DDIs are considered as positive examples and unknown DDIs are considered as negative examples. In the experiment, we used COVID-19 KG and first ran a 10-fold cross-validation test on all positive examples and a set of randomly sampled negative examples, which were 10 times as many as positive samples. This scenario basically mimicked the practical situation of drug repositioning [5]. For each fold, 90% of randomly chosen positive and negative examples were used as the training set, and the remaining 10% of positive and negative examples were regarded as the test set.  
We then compared the performance of Att-GCN-DDI with GCN-DDI, Neo-DTI [5], DTI-NET [6] and HNM [24] in predicting DDIs. GCN-DDI indicates that the GCN model in our framework does not use attention mechanism. The area under the precise recall area curve (AUPR) and the area under receiver operating characteristic curve (AUROC) were used to evaluate the predictive performance of all prediction methods. The evaluation results are shown in Figure 4. The results show that Att-GCN-DDI is superior to other  
Figure 3: Path between Tipranavir and COVID-19.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DrugBank Id** & **Drug Name** & **Source of Clinical Feasibility** \\ \hline DB00300 & Tenofovir & Published medical literature [35] \\ DB01601 & Lopinavir & Experimental drugs for COVID-19 in DrugBank [34] \\ DB01264 & Darunavir & Experimental drugs for COVID-19 in DrugBank [34] \\ DB00503 & Ritonavir & Published Treatment Protocol in China [36] \\ DB00811 & Ribavirin & Published Treatment Protocol in China [36] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clinically proven drugs in our drug candidates.  
methods in AUROC and AUPR. Att-GCN-DDI adopts the Att-GCN model to extract features by node embedding, so the extracted features can better retain the topology structure of nodes. This makes the prediction more accurate.  
Next, we tested Att-GCN-DDI in another scenario by including all positive and negative examples in the 10-fold cross-validation procedure. The evaluation results are shown in Figure 5. We find that the performance of Att-GCN-DDI in this scenario is also superior to other methods. However, the AUPR of GCN-DDI is very close to GCN-DDI. The main reason for this situation is that with the expansion of data volume, the demand of the model for accurate topology feature extraction decreases. Therefore, compared with other methods, the application of GCN for feature extraction has no obvious advantages. From Figures 4 and 5, we can see that the Att-GCN-DDI using attention mechanism performs obviously better than GCN-DDI in two scenarios. This is because the attention mechanism can flexibly capture the relationship between global information and local information. This is very important for extracting feature information of topology structure in the knowledge graph.