It was intended to anticipate protein surface ligand binding locations and use a random forests classifier to assess amino acids as putative ligand binder candidates. Before close-range interactions like hydrophobic and hydrogen bonding occurred, electrostatic interactions helped orient proteins properly. To determine the protein's amino acids and binding site, the graph neural network (GNN) method was utilized. The binding region is displayed in an orange box in Figures , and c in yellow-green color. In addition, the areas of complementary charge distributions-where the negatively charged and positively charged regions of two distinct proteins interact-were also considered by computing the electrostatic surface potential (ESP) of the receptors using the ChimeraX software, as shown in Figure and. The interaction site of the receptors in the black box indicates that the TYRP receptor was mostly negative (red) charged, while CD19 and CD28 were particularly positive (blue). Therefore, charge complementarity also played a significant role in receptor interactions.