Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based models, especially binary classification models, have been developed to predict whether a drug-target pair interacts or not. However, there is still much room for improvement in the performance of current methods. Multi-label learning can overcome some difficulties caused by single-label learning in order to improve the predictive performance. The key challenge faced by multi-label learning is the exponential-sized output space, and considering label correlations can help to overcome this challenge. In this paper, we facilitate multi-label classification by introducing community detection methods for DTI prediction, named DTI-MLCD. Moreover, we updated the gold standard data set by adding 15,000 more positive DTI samples in comparison to the data set, which has widely been used by most of previously published DTI prediction methods since 2008. The proposed DTI-MLCD is applied to both data sets, demonstrating its superiority over other machine learning methods and several existing methods. The data sets and source code of this study are freely available at https://github.com/a96123155/DTI-MLCD.

中文翻译：

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DTI-MLCD：使用多标签学习和社区检测方法预测药物-靶标相互作用。

确定药物靶点相互作用 (DTI) 是药物发现和药物重新定位的重要步骤。为了降低实验成本，已经为此任务提出了大量计算方法。已经开发了基于机器学习的模型，尤其是二元分类模型来预测药物-靶标对是否相互作用。但是，当前方法的性能仍有很大的改进空间。多标签学习可以克服单标签学习带来的一些困难，以提高预测性能。多标签学习面临的主要挑战是指数大小的输出空间，考虑标签相关性有助于克服这一挑战。在本文中，我们通过引入用于 DTI 预测的社区检测方法（称为 DTI-MLCD）来促进多标签分类。此外，与数据集相比，我们通过添加 15,000 个以上的正 DTI 样本更新了黄金标准数据集，自 2008 年以来，大多数先前发布的 DTI 预测方法已广泛使用该数据集。建议的 DTI-MLCD 应用于这两个数据集，证明其优于其他机器学习方法和几种现有方法。本研究的数据集和源代码可在 https://github.com/a96123155/DTI-MLCD 免费获得。提出的 DTI-MLCD 应用于这两个数据集，证明其优于其他机器学习方法和几种现有方法。本研究的数据集和源代码可在 https://github.com/a96123155/DTI-MLCD 免费获得。提出的 DTI-MLCD 应用于这两个数据集，证明其优于其他机器学习方法和几种现有方法。本研究的数据集和源代码可在 https://github.com/a96123155/DTI-MLCD 免费获得。