论文标题
用于药物互动预测的多视图图对比度表示学习
Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction
论文作者
论文摘要
药物互动（DDI）预测是医疗机器学习社区中的重要任务。这项研究提出了一种新方法，多视图图对比度表示，用于药物相互作用预测，简洁的奇迹，以同时捕获分子之间的视图间分子结构和视图内相互作用。奇迹将DDI网络视为多视图图，其中相互作用图中的每个节点本身都是药物分子图实例。我们在奇迹学习阶段分别使用GCN和债券感知信息传递网络来编码DDI关系和药物分子图。另外，我们提出了一个新颖的无监督对比学习组件，以平衡和整合多视图信息。多个真实数据集的全面实验表明，奇迹始终超过最先进的DDI预测模型。
Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community. This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view interactions between molecules simultaneously. MIRACLE treats a DDI network as a multi-view graph where each node in the interaction graph itself is a drug molecular graph instance. We use GCNs and bond-aware attentive message passing networks to encode DDI relationships and drug molecular graphs in the MIRACLE learning stage, respectively. Also, we propose a novel unsupervised contrastive learning component to balance and integrate the multi-view information. Comprehensive experiments on multiple real datasets show that MIRACLE outperforms the state-of-the-art DDI prediction models consistently.
