###### Abstract
In the realm of drug discovery, chemical biology and computational drug design methodologies are harnessed for the effective identification and refinement of lead compounds. Chemical biology focuses primarily on unraveling and elucidating the biological function of a target and understanding the mechanism of action of a chemical modulator. Conversely, the in silico-based method or computer-aided drug design leverages structural insights from the target (structure-based) or known bioactive ligands (ligand-based) to facilitate and accelerate the identification of promising candidate drugs [11]. There has been a shift in the discovery of antimalarial drugs from phenotypic screening to target-based approaches, as more potential drug targets have been validated in Plasmodium species [4]. Given the high attrition rate, high demand for new drugs, and enormous cost and time-consuming nature of drug discovery [12; 13; 14; 15; 16], it is essential to select the targets that are the most likely to deliver progressive drug candidates. Target-based drug discovery is the dominant paradigm of drug discovery [17]. In this study, we utilize the following three potential and high priority targets found in Plasmodium that are responsible and ready to enter the antimalarial drug discovery process. * Acetyl CoA Synthease (PfAcAS; high priority target [4]) is responsible for the biosynthesis of acetyl coenzyme A from coenzyme A and acetate. Researchers have recently reported on the validation of this enzyme [18; 19]. * Bifunctional Farnesyl/Geranylgeranyl Pyrophosphate Synthase (F/GGPPS; high priority target [4]) is a key enzyme in isoprenoid biosynthesis that synthesizes C15 and C20 prenyl chains. Prenyl chains are the substrates of several prenyltransferases that result in isoprenoid products essential for the survival of the parasite [20]. * Monoacylglycerolglycerol Lipase (PfMAGL; new and emerging [4]) [21]. Human monoacylglycerol lipase (MAGL) catalyzes the hydrolysis of a variety of monoglyceries into fatty acids and glycerol. In Plasmodium falciparum, this enzyme has been reported to play a role in the processing of these monoglyceries, including palmitoyl and oleoyl glycerols [21]. The criteria considered important for selecting the aforementioned targets for antimalarial drug discovery are discussed by [PERSON] et al. [4]. They also describe the analysis of several drug targets within the Malaria Drug Accelerator (MalDA) pipeline, allowing them to prioritize targets that are ready to enter the drug discovery phase. In recent years, machine learning (ML) techniques such as graph neural networks (GNN) and natural language processing (NLP), exemplified by methods such as ChemBERta, SMILES-BERT, etc. [22; 23; 24], have shown remarkable performance in various domains such as cheminformatics and bioinformatics [13; 25; 26]. Researchers have actively proposed data-driven approaches and have adopted computational biology perspectives based on deep learning to explore drug-target interaction or relations [14; 15; 16; 27; 28; 29; 30; 31; 32; 33]. Accurate prediction and identification of drug-target interactions (DTIs) are pivotal elements in drug discovery [14; 15; 16; 27; 32; 33]. The emphasis on computational methods for the prediction of DTIs has grown due to the significant costs and time associated with extensive in vivo and in vitro experiments with a wide range of potential drug chemical compounds [13; 14; 15; 16]. On the other hand, advanced DL frameworks, utilizing a variety of GNNs such as graph convolutional networks (GCNs) [34], graph attention networks (GATs) [35], gated graph neural networks (GGNNs) [36], and residual graph convolutional networks (RGCNs) [37], have achieved groundbreaking performance in various research domains, including social and natural sciences, and knowledge graphs [38; 39].