Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a large number of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in terms of performance and scalability. In this paper, we proposed a Network Embedding frameWork in MultIplex Network (NEWMIN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide the quantitative importance of each network. For drug combination prediction, we found seven novel drug combinations that have been validated by external sources among the top-ranked predictions of our model. To verify the feasibility of NEWMIN, we compared NEWMIN with other five methods, for which it showed better performance than other methods in terms of the area under the precision-recall curve and receiver operating characteristic curve.

中文翻译：

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一种基于集成多元网络的药物组合预测网络嵌入框架

药物组合是疾病治疗的明智策略，因为它提高了治疗效果并减少了伴随的副作用。由于候选化合物之间存在大量可能的组合，因此无法进行详尽的筛选。目前，大量研究集中在预测潜在的药物组合上。然而，这些方法在性能和可扩展性方面并不完全令人满意。在本文中，我们提出了一种在Multiplex Network (NEWMIN) 中的网络嵌入框架来预测合成药物组合。基于多重药物相似性网络，我们提供了替代方法来整合来自不同方面的有用信息并确定每个网络的定量重要性。对于药物组合预测，在我们模型的排名靠前的预测中，我们发现了七种已通过外部资源验证的新型药物组合。为了验证NEWMIN的可行性，我们将NEWMIN与其他五种方法进行了比较，在精确召回曲线下面积和接收者操作特征曲线方面表现出优于其他方法的性能。