Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments.

Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model.

Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

背景：识别药物与靶标的相互作用对于发现药物至关重要。预测药物的意外治疗或不良副作用是有益的。迄今为止，已经提出了几种计算方法来预测药物-靶标相互作用，因为与传统的湿法实验相比，它们迅速且成本低。

方法：在这项研究中，我们以不同的方式调查了这个问题。根据KEGG所说，药物根据其靶蛋白分为几类。提出了一种多标签分类模型，可将药物分配到正确的目标人群中。为了充分利用已知的药物特性，构建了五个网络，每个网络代表一个特性中的药物关联。采用一种强大的网络嵌入方法Mashup从上述网络中提取药物特征，在此基础上，几种机器学习算法包括RAndom k-labELsets（RAKEL）算法，Label Powerset（LP）算法和Support Vector Machine（SVM） ），用于建立分类模型。

结果与结论：十倍交叉验证得出的准确度为0.839，精确匹配为0.816，汉明损失为0.037，表明该模型具有良好的性能。还分析了每个网络的贡献。此外，发现具有多个网络的网络模型优于具有单个网络和经典模型的网络模型，表明了所提出模型的优越性。