Brief Bioinform. 2014 Sep;15(5):734-47. doi: 10.1093/bib/bbt056. Epub 2013 Aug 11.
Computationally predicting drug-target interactions is useful to select possible drug (or target) candidates for further biochemical verification. We focus on machine learning-based approaches, particularly similarity-based methods that use drug and target similarities, which show relationships among drugs and those among targets, respectively. These two similarities represent two emerging concepts, the chemical space and the genomic space. Typically, the methods combine these two types of similarities to generate models for predicting new drug-target interactions. This process is also closely related to a lot of work in pharmacogenomics or chemical biology that attempt to understand the relationships between the chemical and genomic spaces. This background makes the similarity-based approaches attractive and promising. This article reviews the similarity-based machine learning methods for predicting drug-target interactions, which are state-of-the-art and have aroused great interest in bioinformatics. We describe each of these methods briefly, and empirically compare these methods under a uniform experimental setting to explore their advantages and limitations.
计算药物-靶标相互作用的预测对于选择可能的药物（或靶标）候选物进行进一步的生化验证是有用的。我们专注于基于机器学习的方法，特别是基于相似性的方法，这些方法分别使用药物和靶标相似性，分别表示药物之间和靶标之间的关系。这两个相似性代表了两个新兴的概念，化学空间和基因组空间。通常，这些方法将这两种类型的相似性结合起来，生成用于预测新的药物-靶标相互作用的模型。这个过程也与试图理解化学空间和基因组空间之间关系的药物基因组学或化学生物学中的许多工作密切相关。这种背景使得基于相似性的方法具有吸引力和前景。本文综述了预测药物-靶标相互作用的基于相似性的机器学习方法，这些方法是最先进的，在生物信息学中引起了极大的兴趣。我们简要描述了这些方法，并在统一的实验设置下对这些方法进行了实证比较，以探索它们的优点和局限性。
