A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes
A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes
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药物解剖治疗化学类别预测和重新定位的混合方法
DOI：
10.1039/c3mb70490d
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发表时间：
2014-01-01
影响因子：
--
通讯作者：
Cai, Yu-Dong
中科院分区：
文献类型：
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作者：
Chen, Lei;Lu, Jing;Cai, Yu-Dong
在解剖治疗化学(ATC)分类系统中，治疗药物根据作用于的器官或系统以及它们的化学、药理和治疗特性被分为14个主要类别。该系统由世界卫生组织(WHO)推荐，为医疗物质分类提供了一个全球标准，并作为国际药物利用研究的工具，以提高药物使用的质量。有鉴于此，有必要开发有效的计算预测方法来识别特定药物的ATC类别，从而有助于对该系统的进一步分析。在这项研究中，我们开始尝试开发一种预测方法，并利用药物化合物的本体信息来获得对药物化合物的预测。针对ATC分类系统中只有约1/4的药物具有本体信息的问题，提出了一种结合药物化合物的本体信息、化学相互作用信息和化学结构信息的混合预测方法来预测药物的ATC类别。结果表明，在训练数据集、内部验证数据集和外部验证数据集中，使用刀切检验对14个主要ATC类的第一次预测准确率分别为75.90%、75.70%和66.36%。对内部和外部验证数据集中出现假阳性预测的一些样本的分析表明，其中一些样本甚至可能与假阳性预测的ATC类有关，这表明这些药物的新用途。可以想象，所提出的方法可以被用作识别ATC类新药或发现已知药物的新用途的有效工具。
In the Anatomical Therapeutic Chemical (ATC) classification system, therapeutic drugs are divided into 14 main classes according to the organ or system on which they act and their chemical, pharmacological and therapeutic properties. This system, recommended by the World Health Organization (WHO), provides a global standard for classifying medical substances and serves as a tool for international drug utilization research to improve quality of drug use. In view of this, it is necessary to develop effective computational prediction methods to identify the ATC-class of a given drug, which thereby could facilitate further analysis of this system. In this study, we initiated an attempt to develop a prediction method and to gain insights from it by utilizing ontology information of drug compounds. Since only about one-fourth of drugs in the ATC classification system have ontology information, a hybrid prediction method combining the ontology information, chemical interaction information and chemical structure information of drug compounds was proposed for the prediction of drug ATC-classes. As a result, by using the Jackknife test, the 1st prediction accuracies for identifying the 14 main ATC-classes in the training dataset, the internal validation dataset and the external validation dataset were 75.90%, 75.70% and 66.36%, respectively. Analysis of some samples with false-positive predictions in the internal and external validation datasets indicated that some of them may even have a relationship with the false-positive predicted ATC-class, suggesting novel uses of these drugs. It was conceivable that the proposed method could be used as an efficient tool to identify ATC-classes of novel drugs or to discover novel uses of known drugs.