The current work used computational simulations to analyze the binding interactions between γδ T-cell receptors and prostate cancer-specific antigens. To understand the binding mechanism of these receptors, we used Graph Neural Networks (GNN) using the Graph Attention Site Prediction (GrASP) web server to find the interaction site. This method gives the probability of amino acids likely interacting with ligands by highlighting the protein's vital regions and displaying likely binding sites represented in yellow and green, Figure . The druggable site is the ability to successfully bind and target ligands/drugs to specific protein locations, allowing for the development of treatments concerning these macromolecules. The binding sites of the three receptors are displayed in yellow-green, as shown in Figure . Electrostatic surface potential is the spread of charges (such as positive, negative, and neutral) across the molecular surface. The γδ receptors are present on the surface of CAR T-cells, and the CD23 and PSCA is located on cancer cells. Therefore, we performed molecular docking simulations to study their interaction and the binding pattern. The docking structures of γδ and PSCA are depicted in Figure , and based on this structure, the receptors strongly bind to the druggable site predicted by the GrASP web server displayed in Figure . These results are promising because they show that both the receptors make head-on interactions with each other, which are essential for proper binding.