## 2 Materials and Methods
### R-GCN Model  
GCNs have gained substantial attention recently [64], emerging as the preferred methodology for learning graph representations to enhance virtual screening for DTI in the pharmaceutical industry [65]. The RGCN is essentially an extension of the GCN architecture, specifically developed to handle the highly multi-relational data found in genuine knowledge bases [66; 67]. In the GCN model, the feature vectors of neighboring nodes undergo a transformation using a shared weight matrix. Conversely, the RGCN introduces the notion of relation types, where the vector transformation is specific to each relation, which means that each type of relation is associated with a unique weight matrix. Consequently, the RGCN model demonstrates the ability to handle relational and heterogeneous graphs effectively [68].  
Herein this study, we introduced antimalarial drugs and three potential Plasmodium antimalarial targets that graphically represent structured network data as shown in Figure 1. These graph data, as discussed in Section 2.3, were used as input to the proposed model that was built based on the RGCN. The RGCN input data are as follows: \(G=(V,\varepsilon,R)\), whereas \(G\) is defined as the graph data, \(v_{i}\in V\) as nodes (entities), and \(v_{i},r,v_{j}\in\varepsilon\) as labeled edges (relations), where \(r\in R\) is defined as a relation type [66]. In this context, it is defined as different types of drug-target relationships (\(E_{R}\)) as shown in Figure 1. The RGCN model was implemented using PyTorch Geometric [69] with hyperparameters as shown in Table 4. The RGCN learns the local and global features of antimalarial drugs and targets, and the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Drugs’ ID** & **Drugs’ SMILES** & **Targets’ Strings** \\ \hline CHEMBL233867 & C1 CS(=O)(=O)C(=O)N1 CC2=CC=CC=C2 & 1 Acetyl CoA Synthease \\  & & 2 Geranylgeranyl Pyrophosphate Synthase \\  & & 3 Monoacylglycerollycerol Lipase \\ CHEMBL295594 & CC(C)C1=NC=C(N1)CCN & Acetyl CoA Synthease \\  & & Geranylgeranyl Pyrophosphate Synthease \\  & & Monoacylglycerollycerol Lipase \\ CHEMBL1224171 & CCCCCCCSS(=O)CCCCCC & Acetyl CoA Synthease \\  & & Geranylgeranyl Pyrophosphate Synthease \\  & & Monoacylglycerollycerol Lipase \\ CHEMBL1214554 & C1=CC(=C(C1C(=NO)N)O)O & Acetyl CoA Synthease \\  & & Geranylgeranyl Pyrophosphate Synthease \\ CHEMBL236690 & CC(=C)[CH]1 CCC(=CC1)COCC\#C & Acetyl CoA Synthease \\  & & Geranylgeranyl Pyrophosphate Synthease \\  & & Monoacylglycerollycerol Lipase \\ \hline \hline \end{tabular}  
* \(PfAcAS_{F}\)—Acetyl CoA Synthease. CC(=O)SCNC(=O)CCNC(=O)CC(=O)C(C(C)C)C(=O)(O)OPC(=O)(O)OCCCCCC (C(C(O)N2C-NC=N3)=C(N=CN=C32)N)OOP(=O)O)O. \(GGPP_{F}\)—Geranylgeranyl Pyrophosphate Synthease. CC(=CCCC(=CCCC(=CCCC(=OO)(O)O(=O)O)C)CC. \({}^{3}\)\(PfAG_{F}\)—Monoacylglycerollycerol Lipase. C1 OC2=C(O1)C=C(C=C2)COC(=O)CCCCCCCC3=CC=C(C=C3)C4=CC=CC=C4.  
\end{table}
Table 3: Data summary used for graph construction.  
combined feature module (i.e., BERT- and Mordred-based features) as shown in Table 5 to predict the antimalarial drugs and potential targets' relations. Drug and target nodes' features were treated as integral components of model parameters, subject to updates during the model training process.