In the present study, we have used computer simulations to examine the interactions between aptamers and the biomarker of cardiovascular disorders, TNF-α protein. We must first comprehend how each aptamer binds to the two receptors to determine the best. Graph neural networks (GNN) using the Graph Attention Site Prediction (GrASP) web server made predicting the druggable or aptamer binding site possible. While standard neural networks handle organized input such as images or sequences, GNNs are able to capture the relationships and interactions between nodes in a graph. This viewpoint sees bonds as edges and atoms as nodes in graphs that represent molecules. The most critical areas of the graph were highlighted by the attention mechanism, indicating potential sites for drug binding. The projected druggable region in Figures and, highlighted in green and yellow, indicated the aptamer binding site. The druggable site showed the protein's ability to effectively target and bind to specific medications. It was crucial to identify druggable sites in order to develop therapies that attach to these macromolecules.