Drug-Target Interaction (DTI) involves the observation and recognition of interactions that occur between chemical molecules and target proteins in the human body. However, lab experimentation for DTI can be time-consuming and repetitive. Approaching these experiments from a computational perspective can save time and increase the scope of research. Graph Neural Networks (GNNs) have emerged as a leading approach in the field of DTI predictions, demonstrating state-of-the-art performance, but there is still scope for improvement in these models with new features. Thus, we propose a novel GNN-based model, GNN-DTI, for DTI predictions. The model takes the chemical molecules (as molecular graphs) and target proteins (as embedding vectors) and predicts a binding affinity score. Due to the complexity of the model, the inner workings of the models are unexplained, and it is challenging to understand the reasoning behind their outputs. Therefore, we also propose a GNN-DTI Explainer model to generate suitable explanations for drug candidates and identify the most important nodes and edges. The proposed model effectively visualizes these explanations for each drug candidate. This allows us to identify the key atoms and their bonds involved during the binding process. It provides further context for the behavior of certain drugs and their interactions with the human body, which can be used to advance drug development and research.