A remarkable aspect is how edge density acts as a structural feature, revealing the local environment of the protein-ligand complex. The closer the interaction (i.e., lower cut-off threshold), the higher the edge density and the higher the importance. Since closer interactions have a more profound impact on binding affinity, this information is embedded to help the network to focus on one of the most crucial aspects of the interaction process. Moreover, incorporating edge density in the graph representation gives the GCN spatial awareness. We intend to give the model a clearer picture of how interactions are distributed throughout the protein-ligand complex. By directly integrating this information into the graph representation, the GCN can learn to assign varying levels of importance, emphasizing edges that represent the denser and more influential interactions, especially those in closer proximity.