Explainable artificial intelligence reveals divergent learning in pharmacophore-based hierarchical pooling graph neural networks

Hierarchical pooling is a promising mechanism to enhance graph neural networks ({GNNs}) by enabling multi-scale representation learning. Rationalization of hierarchical {GNN} predictions remains an underexplored area. In this work, we investigate the impact of hierarchical pooling on {GNNs} for molecular property prediction. We designed architectural variants integrating pharmacophore features with pooling {GNNs} at different levels. {GNN} models with pharmacophore-based graph reduction or hierarchical pooling achieved comparable compound classification performance. Explainable artificial intelligence ({XAI}) methods were applied to compare feature importance and substructure attribution for the different model architectures. Qualitative and quantitative analyses of the resulting explanations demonstrated that the {GNN} variants had different internal learning characteristics. {GNN} models based on reduced graphs matched the prediction accuracy of models based on complete graph representations following different variant-dependent learning strategies.