Concomitant administration of drugs can cause drug-drug interactions (DDIs). Some drug combinations are beneficial, but other ones may cause negative effects which are previously unrecorded. Previous works on DDI prediction usually rely on hand-engineered domain knowledge, which is laborious to obtain. In this work, we propose a novel model, Molecular Substructure-Aware Network (MSAN), to effectively predict potential DDIs from molecular structures of drug pairs. We adopt a Transformer-like substructure extraction module to acquire a fixed number of representative vectors that are associated with various substructure patterns of the drug molecule. Then, interaction strength between the two drugs' substructures will be captured by a similarity-based interaction module. We also perform a substructure dropping augmentation before graph encoding to alleviate overfitting. Experimental results from a real-world dataset reveal that our proposed model achieves the state-of-the-art performance. We also show that the predictions of our model are highly interpretable through a case study.

翻译：在这项工作中,我们提出了一个新型模型,即分子子结构软件网络(MSAN),以有效预测药物配方分子结构的潜在DDI。我们采用了类似变异器的子结构提取模块,以获得与药物分子的各种亚结构模式相关的固定数量的代表性矢量。然后,两种药物的子结构之间的相互作用强度将由一个基于类似特性的互动模块捕捉。我们还在图形编码之前进行一个子结构的递减增,以缓解过度的调整。一个真实世界数据集的实验结果显示,我们提议的模型取得了最先进的性能。我们还表明,我们模型的预测通过案例研究是高度可解释的。