Relating Anatomical Therapeutic Indications by the Ensemble Similarity of Drug Sets

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• 作者：Leihong Wu ; Ni Ai ; Yufeng Liu ; Yi Wang ; Xiaohui Fan
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2013
• 出版时间：August 26, 2013
• 年：2013
• 卷：53
• 期：8
• 页码：2154-2160
• 全文大小：324K
• 年卷期：v.53,no.8(August 26, 2013)
• ISSN：1549-960X

文摘

The anatomical therapeutic chemical (ATC) system is a world standard to define drug indications. Despite its broad applications in pharmaceutical and biomedical research, only a few studies that examine the relationships among ATC classes have been published. Here we present a similarity-based approach, named the indication similarity ensemble approach (iSEA), that innovatively correlates ATC classes by their drug set similarity. Our study demonstrated that iSEA was capable of relating ATC classes, and these relationships could accurately assign the right indications for approved drugs and make reasonable predictions about possible clinical indications for unclassified drugs, which would provide valuable information for drug repositioning. Additionally, on the basis of iSEA, we constructed the first ATC relationship network to reflect correlations among ATCs from a network view, which would further render novel insight to understand the intrinsic relationships in the ATC system.