The pharmacological activity of one drug may be changed unexpectedly, owing to the concurrent administration of another drug. It is likely to cause unexpected drug-drug interactions (DDIs). Several machine learning approaches have been proposed to predict the occurrence of DDIs. However, existing approaches are almost dependent heavily on various drug-related features, which may incur noisy inductive bias. To alleviate this problem, we investigate the utilization of the end-to-end graph representation learning for the DDI prediction task. We establish a novel DDI prediction method named GCN-BMP (Graph Convolutional Network wth Bond-aware Message Propagation) to conduct the accurate prediction for DDIs. Our experiments on two real-world datasets demonstrate that GCN-BMP can achieve higher performance when compared to various baseline approaches. Moreover, in the light of the self-contained attention mechanism in our GCN-BMP, we could find the most vital local atoms which are conforming to domain knowledge with certain interpretability.

中文翻译：

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GCN-BMP：研究 DDI 预测任务的图表示学习

由于同时服用另一种药物，一种药物的药理活性可能会发生意外变化。它可能会导致意外的药物相互作用 (DDI)。已经提出了几种机器学习方法来预测 DDI 的发生。然而，现有的方法几乎严重依赖于各种与药物相关的特征，这可能会导致嘈杂的归纳偏差。为了缓解这个问题，我们研究了端到端图表示学习在 DDI 预测任务中的利用。我们建立了一种名为 GCN-BMP（具有键感知消息传播的图形卷积网络）的新型 DDI 预测方法来对 DDI 进行准确预测。我们在两个真实世界数据集上的实验表明，与各种基线方法相比，GCN-BMP 可以实现更高的性能。