Graph Neural Networks (GNNs) have demonstrated strong performance in molecular interaction prediction, but their interpretability remains limited, especially in domain-specific applications like ligand-receptor modeling. This paper presents a model-agnostic explainer for GNN-CLS, a specialized GNN model designed to predict interactions between molecules and olfactory receptor proteins. The proposed method uses cooperative game theory to identify influential molecular substructures and receptor sequence regions, offering faithful and theoretically grounded explanations of model predictions. This approach enhances transparency by revealing which features drive predictive outcomes, helping bridge the gap between model performance and chemical insight. The contributions include a formal framework for relevance attribution and interaction analysis, positioning this work at the intersection of explainable AI and computational chemistry.

Graph Neural Networks (GNNs) are very efficient at classifying graphs but their internal functioning is opaque which limits their field of application. Existing methods to explain GNN focus on disclosing the relationships between input graphs and model decision. In this article, we propose a method that goes further and isolates the internal features, hidden in the network layers, that are automatically identified by the GNN and used in the decision process. We show that this method makes possible to know the parts of the input graphs used by GNN with much less bias that SOTA methods and thus to bring confidence in the decision process.