###### Abstract
These frameworks have shown their effectiveness in various applications, such as molecular properties prediction [40], widespread application in predicting DTIs [12], and the prediction of protein function [41] within the realm of biochemical problems where the interactions can be represented as graph-like structured data. Specifically, GCNs have found application in addressing pharmacological similarities by considering both sequential and structural properties [12]. The graph representations of biochemical entities have proven capable of capturing structural features akin to Euclidean ones, eliminating the need for extensive feature engineering [42; 43]. GNNs have been demonstrated to be relatively powerful in modeling their formally input graph-structured data to the DL pipeline [44; 45; 46]. The graph-structured data are basically presented as \(G=(V,E,u)\), where \(G\) is a graph, \(V\) indicates vertices or nodes, \(E\) means edges comprising adjacency (A) with weights (W), and \(u\) indicates feature vectors of the nodes. All GNN layers are implemented via a message passing network (MPN) interface or framework [47]. Quantitative Structure-Activity Relationship (QSAR) modeling investigates the relationship between the chemical structure of molecules or substances and their biological activities towards a specific target (i.e., a drug target) [48]. By employing statistical techniques, QSAR and ML models have the capacity to predict drugs for drug repurposing or repositioning and pharmacological activity of novel compounds based on their structural attributes [49]. This capability enables chemists to strategically modify molecules, improving the potency of a drug or alleviating side effects, resulting in a more cost-effective and time-efficient drug development process [50]. Herein, this study introduces a pioneering approach that harnesses the potential of advanced ML methodologies to predict and identify antimalarial drugs. The essence of this approach is the integration of state-of-the-art NLP and GNN techniques. The study proposes a fusion of the SMILES-based bidirectional encoder representations from transformers (BERT) model, an NLP model, with the robust capabilities of the GNN model (i.e., relational graph convolutional network (RGCN)). We adopted the deepchem SmilesTokenizer module for embedding features generation [51] that were later treated by RGCN as node features. This tokenizer heavily inherits from the BertTokenizer class found in Huggingface's transformers library. It runs a WordPiece tokenization algorithm over SMILES strings using the tokenization SMILES regex developed by [PERSON] et al. [52]. Furthermore, the RGCN model was trained using multi-relational antimalarial drugs and Plasmodium potential targets graph data, where nodes represent antimalarial drugs and potential targets, and edges represent drug-target relations. The contributions of this study can be summarized as follows:  
* Apparently, this is the first study to introduce the NLP-based applications to chemistry and biology (i.e., BERT-based SmilesTokenizer) and the GNN model (i.e., RGCN) for the antimalarial drug prediction against Plasmodium falciparum. * Secondly, the graph-based network was designed between antimalarial drugs and three potential new targets for antimalarial drugs found in Plasmodium as shown in Figure 1.  
We further developed three independent models, BERT-RGCN, Mordred-RGCN, and BERT-Mordred-RGCN, and compared their performance to understand the contributions of the features. * Lastly, various experiments were performed using features of individual antimalarialarial drugs, followed by combination of features of antimalarialarial drugs and three potential targets for malaria drugs found in Plasmodium.