In the process of drug discovery and disease treatment, drug repositioning is broadly studied to identify biological targets for existing drugs. Many methods have been proposed for drug-target interaction prediction by taking into account different kinds of data sources. However, most of the existing methods only use one side information for drugs or targets to predict new targets for drugs. Some recent works have improved the prediction accuracy by jointly considering multiple representations of drugs and targets. In this work, the authors propose a drug-target prediction approach by matrix completion with multi-view side information (MCM) of drugs and proteins from both structural view and chemical view. Different from existing studies for drug-target prediction, they predict drug-target interaction by directly completing the interaction matrix between them. The experimental results show that the MCM method could obtain significantly higher accuracies than the comparison methods. They finally report new drug-target interactions for 26 FDA-approved drugs, and biologically discuss these targets using existing references.

中文翻译：

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通过具有多视图边信息的矩阵完成重新定位药物。

在药物发现和疾病治疗过程中，药物重新定位被广泛研究以识别现有药物的生物靶点。通过考虑不同类型的数据源，已经提出了许多用于药物-靶标相互作用预测的方法。然而，大多数现有方法仅使用药物或靶标的一个侧面信息来预测药物的新靶标。最近的一些工作通过联合考虑药物和目标的多种表示来提高预测准确性。在这项工作中，作者提出了一种药物靶点预测方法，该方法通过矩阵补全与药物和蛋白质的多视图边信息 (MCM) 从结构视图和化学视图进行。与现有的药物靶点预测研究不同，他们通过直接完成它们之间的相互作用矩阵来预测药物与靶点的相互作用。实验结果表明，与比较方法相比，MCM方法可以获得明显更高的精度。他们最终报告了 26 种 FDA 批准药物的新药物-靶点相互作用，并使用现有参考文献对这些靶点进行了生物学讨论。