In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets and evaluation protocols. In addition, we emphasize possible high impact applications and important future research directions in this domain.

翻译：近年来,提出了许多机器学习模型,试图解决聚药副作用识别、药物-药物互动预测和综合疗法设计任务。在这里,我们对能够解决这些任务的关系机学习模型提出了统一的理论观点。我们提供了基本定义,比较了现有的模型结构并讨论了性能衡量标准、数据集和评价程序。此外,我们强调这一领域可能具有高度影响的应用和未来的重要研究方向。