## 4 Results
### Model Testing and Evaluation  
The RGCN models (i.e., BERT-RGCN, which utilized Bert-based features, and Mordred-RGCN, which utilized Mordred-based features) after 10-fold cross-validation training with prepared graph data, were therefore tested and evaluated. The testing results of the models are shown in Table 8.  
RGCN Model Test Performance Featuring BERT-Based and Mordred-Based Features  
Test results for the proposed method (i.e., BERT-RGCN model) are presented in Table 8. The presented table contains test results for the BERT-RGCN model, detailing its performance in various combinations of features. Each row corresponds to a specific performance metric, while each column represents a different scenario with distinct sets of features. Below is a detailed explanation of each metric. Accuracy represents the overall correctness of the RGCN models' (i.e., BERT-RGCN and Mordred-RGCN) predictions. Accuracy performances were higher score values, indicating better accuracy. The BERT-RGCN and Mordred-RGCN models achieved high accuracy consistently across all combination of features, ranging from 0.9917 to 0.9972 and 0.9958 to 0.9972, respectively, as shown in Table 8. Sensitivity, also known as True Positive Rate or Recall, measures the ability of the model to correctly identify positive instances. This indicates the proportion of actual positive instances correctly classified by RGCN models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Test result** & \(AmD_{F}\) & \(AmD_{F}\) & \(AmD_{F}\) and & \(AmD_{F}\) and & \(AmD_{F}\) and \(Ts_{F}\) \\  & & \(PfAcA_{F}\) & \(AGPP_{F}\) & \(PfMAGL_{F}\) & \(AmD_{F}\) and & \(Ts_{F}\) \\ \hline BERT-RGCN & Accuracy & 0.9972 & 0.9972 & 0.9917 & 0.9972 & 0.9958 \\  & Sensitivity & 0.9968 & 0.9968 & 0.9839 & 0.9968 & 0.9968 \\  & Specificity & 0.9976 & 0.9976 & 0.9976 & 0.9976 & 0.9951 \\  & MCC & 0.9943 & 0.9943 & 0.9831 & 0.9943 & 0.9915 \\  & AUROC & 0.9971 & 0.9974 & 0.9977 & 0.9965 & 0.9942 \\  & AUPRC & 0.9960 & 0.9961 & 0.9972 & 0.9923 & 0.9896 \\ \hline Mordred-RGCN & Accuracy & 0.9958 & 0.9958 & 0.9972 & 0.9958 & 0.9958 \\  & Sensitivity & 0.9968 & 0.9968 & 0.9968 & 0.9968 & 0.9968 \\  & Specificity & 0.9951 & 0.9951 & 0.9976 & 0.9951 & 0.9951 \\  & MCC & 0.9915 & 0.9915 & 0.9943 & 0.9915 & 0.9915 \\  & AUROC & 0.9995 & 0.9991 & 0.9990 & 0.9986 & 0.9971 \\  & AUPRC & 0.9996 & 0.9992 & 0.9991 & 0.9986 & 0.9973 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation experimental results and independent test performance on BERT features. In Table 8, the BERT-RGCN model demonstrated high sensitivity, ranging from 0.9839 to 0.9968. Meanwhile, the Mordred-RGCN model consistently achieved a high sensitivity of 0.9968 across all features. Specificity measures the ability of RGCN-based models to correctly identify negative instances. This indicates the proportion of actual negative instances correctly classified by the proposed methods (i.e., BERT-RGCN and Mordred-RGCN models), and values close to 1 signify promising performance. The BERT-RGCN model consistently showed high specificity, ranging from 0.9951 to 0.9976. Consequently, Mordred-RGCN also had a similar performance of 0.9951 across all features and 0.9976 for the remaining combination of features, as shown in Table 8.  
In this study, MCC takes into account true and false positives and negatives, providing a balanced measure even for imbalanced datasets. The range of values achieved by BERT-RGCN (i.e., MCC values ranging from 0.9831 to 0.9943) and Mordred-RGCN (i.e., 0.9943 for \(AmD_{F}\) and \(GGPP_{F}\) and 0.9915 for other features) were high, which signifies perfect predictions of antimalarial drugs of the models as shown in Table 8.  
Furthermore, the models' performances in this study were evaluated through the implemented AUROC metric. AUROC evaluates the trade-off between the true positive rate and the false positive rate across different threshold settings. Values close to 1 resemble higher values, which signify better discrimination.