论文标题
用于药物相互作用预测的分子亚结构感知网络
Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction
论文作者
论文摘要
伴随的药物给药会引起药物 - 药物相互作用（DDIS）。某些药物组合是有益的，但其他药物组合可能会引起以前未记录的负面影响。以前关于DDI预测的工作通常依赖于手工设计的领域知识，这很费力地获得。在这项工作中，我们提出了一种新型模型，即分子亚结构感知网络（MSAN），以有效预测药物对分子结构的潜在DDI。我们采用类似变压器的子结构提取模块来获取与药物分子的各种子结构模式相关的固定代表媒介。然后，两个药物的子结构之间的相互作用强度将由基于相似性的相互作用模块捕获。在图形编码之前，我们还执行一个子结构删除增强，以减轻过度拟合。实际数据集的实验结果表明，我们提出的模型实现了最新的性能。我们还表明，通过案例研究，我们的模型的预测是高度解释的。
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.
