Chen Fan-Shu, Jiang Zhen-Ran
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China; Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China.
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China; Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China.
J Biomed Inform. 2015 Dec;58:80-88. doi: 10.1016/j.jbi.2015.09.016. Epub 2015 Oct 3.
Predicting Anatomical Therapeutic Chemical (ATC) code of drugs is of vital importance for drug classification and repositioning. Discovering new association information related to drugs and ATC codes is still difficult for this topic. We propose a novel method named drug-domain hybrid (dD-Hybrid) incorporating drug-domain interaction network information into prediction models to predict drug's ATC codes. It is based on the assumption that drugs interacting with the same domain tend to share therapeutic effects. The results demonstrated dD-Hybrid has comparable performance to other methods on the gold standard dataset. Further, several new predicted drug-ATC pairs have been verified by experiments, which offer a novel way to utilize drugs for new purposes effectively.
预测药物的解剖学治疗学化学（ATC）编码对于药物分类和重新定位至关重要。对于该主题而言，发现与药物和ATC编码相关的新关联信息仍然具有挑战性。我们提出了一种名为药物-结构域混合（dD-Hybrid）的新方法，该方法将药物-结构域相互作用网络信息纳入预测模型以预测药物的ATC编码。它基于这样的假设，即与相同结构域相互作用的药物往往具有共同的治疗效果。结果表明，dD-Hybrid在金标准数据集上与其他方法具有相当的性能。此外，一些新预测的药物-ATC对已通过实验验证，这为有效利用药物实现新用途提供了一种新方法。
