## 4 Experimental Setup and Results Analysis
### Experimental Results  
In this experiment, DDI-SSL uses the following metrics: ACC, AUC-ROC, and F1-score. For the multiclass models (MR-GNN and DeepDDI), the microaverage measure of AUC is considered. Every technique is deployed on an identical dataset partition. Concerning MR-GNN, the initial code proffered by its creators is redeployed. DeepDDI and MHCADDI methods are also reimplemented according to the settings given in their respective original papers. The scores obtained by each method for each metric are shown in the bottom row of Table 3. The DDI-SSL algorithm performs well on all three datasets, including DrugBank, where it shows improvement in scores, while DeepSynergy showcases superior AUC outcomes on the DrugBank collection, its measures hold questionable reliability given the dataset's skewed distribution. Even with very low true positive rates, multiclass classifiers often have high AUC scores.  
In the DrugBank dataset, each DDI pair is treated as a positive sample while generating a negative sample. However, each DDI tuple has only one interaction. As a result, AUC-ROC may not be sensitive to the performance of the minority class because it relies on the calculation of true positive rate and false positive rate. When the number of negative samples outweighs the number of positive samples, even if the model performs poorly on the positive class, it can lead to a relatively low false positive rate, thereby overestimating the model's performance. DDI-SSL also has a very high AUC score, indicating that it not only distinguishes interacting and noninteracting drugs but also has high accuracy  
Additionally, when considering the TwoSides and DrugComb collections, DDI-SSL surpasses numerous alternative techniques in F1-score and AUC metrics. The accuracy (ACC) of various approaches on the evaluation set is depicted in Figure 2. On the DrugBank dataset, the DDI-SSL method shows improvement over DeepDDI, DeepSynergy, and others and converges more smoothly, ultimately approaching 100% accuracy. On the TwoSides dataset, the accuracy is improved, and the convergence is also faster. On the DrugComb dataset, the performance is more oscillatory, and since multiple methods have oscillating ACC curves on this dataset, this suggests that the dataset itself has significant differences in distribution. Furthermore, our observations revealed that DDI-SSL maintains commendable precision on the DrugCombDB collection, signifying the model's robust consistency when applied to such datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Encoder** & **Hyperparameter** \\ \hline GCN-BMP & GCN & Drug encoder channels \\  & Hidden layer channels \\ \hline DeepDDS & GCN or GAT & Context encoder channels \\  & Hidden layer channels \\ \hline MatchMaker & Feedforward & Drug encoder channels \\  & Hidden layer channels \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**DrugBank**} & \multicolumn{2}{c}{**Tweosides**} & \multicolumn{2}{c}{**DrugComb**} & \multicolumn{2}{c}{**DrugCombDB**} \\ \cline{2-9}  & **DrugBank F1** & **DrugBank Acc** & **Tweosides F1** & **Tweosides Acc** & **DrugComb F1** & **DrugComb Acc** & **DrugComb FB1** & **DrugComb ABC** \\ \hline DeepDOI & 0.715 \(\pm\) 0.003 & 0.880 \(\pm\) 0.002 & 0.848