This study centered around a computational analysis conducted to identify an antibody that strongly binds to the IL-13Rα2 receptor, providing information that allows for advancements in specific CAR T-cell therapies for glioblastoma multiforme (GBM). AlphaFold 3, an AI technology, was used to create the 3D model of the IL-13Rα2 protein, shown in Figure . On the surface of these receptors, specific amino acids are responsible for binding to substrate/ligands. To find these amino acids, we used the graph neural network method to identify specific druggable/binding sights where the antibody could bind to the receptor. Once complete, the software highlights the potential binding site, as shown in yellow-green in Figure . Identifying these druggable sites provides essential information for advancing the field of designing therapeutics as they represent the protein's capacity to target and bind to drugs efficiently. In addition, understanding these receptors' charge (electrostatic surface potential, ESP) is significant in identifying the right antibodies. The ESP shows that although the entire protein is negatively charged, the interleukin receptor's binding site and the antibody's variable region were primarily neutral.