The prediction of drug side effects is critical in drug discovery and development to guarantee patient safety and therapeutic effectiveness. Traditional drug targeting methods emphasize potential side effects but do not consider affected tissues and organs or body parts. In this study, we are proposing a new approach utilizing a graph neural network (GNN)-based model that predicts not just the side effects of drugs but also the organs and tissues affected by them. Our model constructs an interactive graph for drug interplay by enriching the data obtained from diversified sources such as SIDER, PubChem, DrugBank, and BioMart data. The findings give hope for progress in more effective drug safety evaluations and the potential to improve personalized medicines through the prediction of tissue and organ responses to medications.