Graph Neural Networks (GNNs) have shown remarkable success in molecular tasks, yet their interpretability remains challenging. Traditional model-level explanation methods like XGNN and GNNInterpreter often fail to identify valid substructures like rings, leading to questionable interpretability. This limitation stems from XGNN's atom-by-atom approach and GNNInterpreter's reliance on average graph embeddings, which overlook the essential structural elements crucial for molecules. To address these gaps, we introduce an innovative \textbf{M}otif-b\textbf{A}sed \textbf{G}NN \textbf{E}xplainer (MAGE) that uses motifs as fundamental units for generating explanations. Our approach begins with extracting potential motifs through a motif decomposition technique. Then, we utilize an attention-based learning method to identify class-specific motifs. Finally, we employ a motif-based graph generator for each class to create molecular graph explanations based on these class-specific motifs. This novel method not only incorporates critical substructures into the explanations but also guarantees their validity, yielding results that are human-understandable. Our proposed method's effectiveness is demonstrated through quantitative and qualitative assessments conducted on six real-world molecular datasets.

翻译：图神经网络（GNN）在分子任务中取得了显著成功，但其可解释性仍面临挑战。传统的模型级解释方法（如XGNN和GNNInterpreter）通常无法识别有效的子结构（例如环），导致可解释性存疑。这一局限性源于XGNN的逐原子生成方式以及GNNInterpreter对平均图嵌入的依赖，两者均忽略了分子中至关重要的结构要素。为弥补这些不足，我们提出一种创新的基于基序的图神经网络解释器（MAGE），将基序作为生成解释的基本单元。该方法首先通过基序分解技术提取潜在基序，随后利用基于注意力的学习方法识别类别特异性基序，最后针对每个类别采用基序驱动的图生成器，基于这些类别特异性基序构建分子图解释。这一新方法不仅将关键子结构纳入解释，还确保了其有效性，生成的结果具有人类可理解性。通过在六个真实分子数据集上进行的定量与定性评估，我们验证了所提方法的有效性。