CHEM: Causally and Hierarchically Explaining Molecules

Graph Neural Networks (GNNs) have significantly advanced in analyzing graph-structured data; however, their explainability remains challenging, affecting their applicability in critical domains such as medicine and pharmacology. In particular, violating the subgraph structure can degrade model interpretability and generalization performance. To address this problem, we propose a hierarchical and explainable causal inference-based GNN. Our model selects features based on explainable subgraph units informed by prior knowledge. Our method begins by clustering molecules into functional groups via the BRICS algorithm, then constructing a hierarchical structure at both the node and motif levels. The proposed model employs a gate module that distills causal features on the motif level and a loss function that disconnects information flow from non-causal features to the target level. The classification results on real-world molecular graphs demonstrate that our model outperforms other causal inference-based GNN models. In addition, it is confirmed that leveraging molecular docking data effectively identifies true causal substructures in the proposed model.