Adverse reactions caused by drug combinations are an increasingly common
phenomenon, making their accurate prediction an important challenge in modern
medicine. However, the polynomial nature of this problem renders lab-based
identification of adverse reactions insufficient. Dozens of computational
approaches have therefore been proposed for the task in recent years, with
varying degrees of success. One group of methods that has seemingly been
under-utilised in this area is tensor factorisation, despite their clear
applicability to this type of data. In this work, we apply three such models to
a benchmark dataset in order to compare them against established techniques. We
find, in contrast to previous reports, that for this task tensor factorisation
models are competitive with state-of-the-art graph neural network models and we
recommend that future work in this field considers cheaper methods with linear
complexity before running costly deep learning processes.

药物联合使用引起的不良反应是现代医学中一个日益普遍的现象，对其准确预测的挑战至关重要。尽管存在大量计算方法来解决这个问题，但由于问题的多项式特性，仅依靠实验室的方法无法满足需求。我们将三种张量分解模型应用于基准数据集，与现有技术进行比较，发现与先前的报告相反，在此任务中，张量分解模型与最先进的图神经网络模型具有竞争力，因此建议未来的研究在运行昂贵的深度学习流程之前考虑更廉价的具有线性复杂度的方法。