While molecular docking continues to be a transformative tool in drug discovery, several limitations remain due to the approximative nature of scoring functions and the neglection of flexibility, among others , . Machine learning (ML) methods, on the other hand, have been introduced in the last decade to tackle molecular docking challenges . For example, Graph Neural Networks (GNNs) have been widely explored to characterize ligand-protein interactions . Several GNN models have been used for ligand-protein affinity prediction, such as CurvAGN , PIGNet , GenScore and SS-GNN , reporting strong correlations between predicted and experimental affinities , , .