## 5 Discussion  
The integration of NLP, represented by the SMILES-based BERT model, and GNN, exemplified by RGCN, presents a novel and potent approach to identify novel antimalarial drug candidates. The use of these advanced ML techniques has shown promise in overcoming the complexities associated with drug-target interactions, offering a pathway towards combating the challenges posed by drug resistance in malaria treatment. The successful prediction of potential antimalarial drugs validated the efficacy of our proposed approach. By harnessing multi-relational data that encompass critical antimalarial drug targets such as PfAcAS, F/GGPPS, and PMMAGL, our proposed models such as BERT-RGCN and Mordred-RGCN demonstrated their efficacy in capturing multi-drug-target relations, showcasing the potential of the proposed approaches in uncovering novel drug candidates with varying modes of action against malaria. The prediction accuracy, validated through rigorous testing and cross-validation methodologies, substantiates the reliability and robustness of the proposed frameworks. This capability is perhaps pivotal in uncovering novel compounds capable of disrupting the malaria parasite's life cycle at various stages.  
Comprehensive analysis of graph-structured data revealed various drug-target interactions, shedding light on potential mechanisms of action and the polypharmacology essential in addressing the complexity of malaria treatment. The ability of the RGCN models such as BERT-RGCN and Mordred-RGCN to identify and infer within complex relational data allowed the identification of previously unexplored antimalarial drug-target interactions, presenting their potential and a foundation for further exploration and experimentation for accelerating drug development in the realm of antimalarial drug discovery.  
However, it is important to acknowledge certain limitations. The availability of comprehensive and curated datasets remains a challenge in drug discovery research. Despite the success of the models (i.e., BERT-RGCN and Mordred-RGCN) in navigating complex relational data, the quality and depth of available data profoundly influence predictive accuracy. Additionally, the need for experimental validation of predicted drug candidates remains imperative to confirm their efficacy and safety profiles. Moreover, while our approach showcases promise, further refinement and optimization are necessary. Fine-tuning the model architecture and leveraging larger and more diverse datasets could enhance the prediction accuracy and broaden the scope of potential drug candidates identified.  
In conclusion, the effectiveness of the integrated BERT model and RGCN in predicting potential antimalarial drugs offers a promising avenue to accelerate drug discovery efforts and address the challenges posed by drug resistance in the fight against malaria. The results also showed that the use of new potential targets such as PfAcAS, F/GGPPS, and PfMAGL for antimalarial drugs regardless of the extraction of features was promising and with the use of graphs to express antimalarial drug molecules and potential targets in Plasmodium, RGCN yielded better results. The success in predicting drug candidates against Plasmodium highlights the potential impact of advanced ML in addressing global health challenges. Moving forward, continued advances in methodologies and collaborative efforts between computational and experimental research are crucial in translating predictions into tangible therapeutic interventions to combat malaria effectively.