Computational Prediction of Drug-Target Interactions Using Chemical, Biological, and Network Features
Computational Prediction of Drug-Target Interactions Using Chemical, Biological, and Network Features
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DOI：
10.1002/minf.201400009
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发表时间：
2014-10-01
影响因子：
3.6
通讯作者：
Chen, Alex F.
中科院分区：
文献类型：
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作者：
Cao, Dong-Sheng;Zhang, Liu-Xia;Chen, Alex F.
药物-靶点相互作用（DTI）是当前药物发现过程的核心。一直致力于开发预测DTI和药物-靶标相互作用网络的方法。现有的方法主要集中在药物或蛋白质结构特征信息的应用上。在目前的工作中，我们提出了一种计算方法的DTI预测相结合的信息，从化学，生物和网络的属性。该方法是基于随机森林（RF）的学习算法结合集成功能预测DTI。人体内涉及酶、离子通道、G蛋白偶联受体（GPCR）和核受体的四类药物-靶标相互作用网络被独立地用于建立预测模型。RF模型对4个药学有用数据集的预测准确率分别为93.52%、94.84%、89.68%和84.72%。我们的方法的预测能力是可比的，甚至优于其他DTI预测方法。这些比较结果表明，相关的网络拓扑结构的信息来源，用于预测DTI。进一步的分析证实，在我们排名最高的DTI预测中，有几个DTI得到了数据库的支持，而其他的则代表了新的潜在DTI。我们相信，我们提出的方法可以帮助限制DTI的搜索空间，并为重新定位旧药物和识别靶点提供一种新的方法。
Drug-target interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drug-target interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68% and 84.72% for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.