Effective representation of molecules is a crucial step in AI-driven drug design and drug discovery, especially for drug-drug interaction (DDIs) prediction. Previous work usually models the drug information from the drug-related knowledge graph or the single drug molecules, but the interaction information between molecular substructures of drug pair is seldom considered, thus often ignoring the influence of bond information on atom node representation, leading to insufficient drug representation. Moreover, key molecular substructures have significant contribution to the DDIs prediction results. Therefore, in this work, we propose a novel Graph learning framework of Mutual Interaction Attention mechanism (called GMIA) to predict DDIs by effectively representing the drug molecules. Specifically, we build the node-edge message communication encoder to aggregate atom node and the incoming edge information for atom node representation and design the mutual interaction attention decoder to capture the mutual interaction context between molecular graphs of drug pairs. GMIA can bridge the gap between two encoders for the single drug molecules by attention mechanism. We also design a co-attention matrix to analyze the significance of different-size substructures obtained from the encoder-decoder layer and provide interpretability. In comparison with other recent state-of-the-art methods, our GMIA achieves the best results in terms of area under the precision-recall-curve (AUPR), area under the ROC curve (AUC), and F1 score on two different scale datasets. The case study indicates that our GMIA can detect the key substructure for potential DDIs, demonstrating the enhanced performance and interpretation ability of GMIA.

中文翻译：

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用图互作用注意力机制预测药物相互作用

分子的有效表示是人工智能驱动的药物设计和药物发现的关键步骤，特别是对于药物相互作用（DDI）预测。以往的工作通常从药物相关的知识图谱或单个药物分子中对药物信息进行建模，但很少考虑药物对的分子子结构之间的相互作用信息，从而往往忽略了键信息对原子节点表示的影响，导致不足药物代表。此外，关键分子子结构对 DDI 预测结果有显着贡献。因此，在这项工作中，我们提出了一种新颖的相互交互注意机制（称为GMIA）的图学习框架，通过有效地表示药物分子来预测DDI。具体来说，我们构建了节点边缘消息通信编码器来聚合原子节点和原子节点表示的传入边缘信息，并设计了相互交互注意解码器来捕获药物对分子图之间的相互交互上下文。 GMIA 可以通过注意力机制弥合单个药物分子的两个编码器之间的差距。我们还设计了一个共同注意矩阵来分析从编码器-解码器层获得的不同大小子结构的重要性并提供可解释性。与其他最新的最先进方法相比，我们的 GMIA 在精确回忆曲线下面积（AUPR）、ROC 曲线下面积（AUC）和两种不同的 F1 分数方面取得了最佳结果。规模数据集。案例研究表明，我们的 GMIA 可以检测潜在 DDI 的关键子结构，证明了 GMIA 增强的性能和解释能力。