Drug–target interaction prediction is important for drug development and drug repurposing. Many computational methods have been proposed for drug–target interaction prediction due to their potential to the time and cost reduction. In this review, we introduce the molecular docking and machine learning-based methods, which have been widely applied to drug–target interaction prediction. Particularly, machine learning-based methods are divided into different types according to the data processing form and task type. For each type of method, we provide a specific description and propose some solutions to improve its capability. The knowledge of heterogeneous network and learning to rank are also summarized in this review. As far as we know, this is the first comprehensive review that summarizes the knowledge of heterogeneous network and learning to rank in the drug–target interaction prediction. Moreover, we propose three aspects that can be explored in depth for future research.

中文翻译：

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药物-靶点相互作用预测的现状和未来展望

药物-靶点相互作用预测对于药物开发和药物再利用很重要。由于它们有可能减少时间和成本，因此已经提出了许多用于药物-靶标相互作用预测的计算方法。在这篇综述中，我们介绍了分子对接和基于机器学习的方法，这些方法已广泛应用于药物-靶标相互作用预测。具体而言，基于机器学习的方法根据数据处理形式和任务类型分为不同的类型。对于每种类型的方法，我们都提供了具体的描述并提出了一些解决方案来提高其能力。这篇综述还总结了异构网络和学习排序的知识。据我们了解，这是第一篇总结异构网络知识和学习在药物-靶标相互作用预测中排名的综合综述。此外，我们提出了三个可以深入探讨以供未来研究的方面。