Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China.
Bioinformatics. 2013 May 15;29(10):1317-24. doi: 10.1093/bioinformatics/btt158. Epub 2013 Apr 5.
Discovering drug's Anatomical Therapeutic Chemical (ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action. However, few studies attempt to annotate drug's potential ATC-codes by computational approaches.
Here, we introduce drug-target network to computationally predict drug's ATC-codes and propose a novel method named NetPredATC. Starting from the assumption that drugs with similar chemical structures or target proteins share common ATC-codes, our method, NetPredATC, aims to assign drug's potential ATC-codes by integrating chemical structures and target proteins. Specifically, we first construct a gold-standard positive dataset from drugs' ATC-code annotation databases. Then we characterize ATC-code and drug by their similarity profiles and define kernel function to correlate them. Finally, we use a kernel method, support vector machine, to automatically predict drug's ATC-codes. Our method was validated on four drug datasets with various target proteins, including enzymes, ion channels, G-protein couple receptors and nuclear receptors. We found that both drug's chemical structure and target protein are predictive, and target protein information has better accuracy. Further integrating these two data sources revealed more experimentally validated ATC-codes for drugs. We extensively compared our NetPredATC with SuperPred, which is a chemical similarity-only based method. Experimental results showed that our NetPredATC outperforms SuperPred not only in predictive coverage but also in accuracy. In addition, database search and functional annotation analysis support that our novel predictions are worthy of future experimental validation.
In conclusion, our new method, NetPredATC, can predict drug's ATC-codes more accurately by incorporating drug-target network and integrating data, which will promote drug mechanism understanding and drug repositioning and discovery.
NetPredATC is available at http://doc.aporc.org/wiki/NetPredATC.
ycwang@nwipb.cas.cn or ywang@amss.ac.cn
Supplementary data are available at Bioinformatics online.
在分子水平上发现药物的解剖治疗化学（ATC）分类规则对于理解绝大多数药物的作用至关重要。然而，很少有研究尝试通过计算方法来注释药物的潜在 ATC 编码。
在这里，我们引入药物-靶标网络来计算预测药物的 ATC 编码，并提出了一种名为 NetPredATC 的新方法。我们的方法 NetPredATC 基于这样的假设，即具有相似化学结构或靶蛋白的药物具有共同的 ATC 编码，旨在通过整合化学结构和靶蛋白来分配药物的潜在 ATC 编码。具体来说，我们首先从药物 ATC 编码注释数据库中构建一个黄金标准阳性数据集。然后，我们通过它们的相似性轮廓来描述 ATC 编码和药物，并定义核函数来关联它们。最后，我们使用核方法，支持向量机，自动预测药物的 ATC 编码。我们的方法在包含酶、离子通道、G 蛋白偶联受体和核受体等各种靶蛋白的四个药物数据集上进行了验证。我们发现，药物的化学结构和靶蛋白都是可预测的，并且靶蛋白信息具有更好的准确性。进一步整合这两个数据源为药物揭示了更多经过实验验证的 ATC 编码。我们广泛比较了我们的 NetPredATC 与 SuperPred，这是一种仅基于化学相似性的方法。实验结果表明，我们的 NetPredATC 在预测覆盖率和准确性方面都优于 SuperPred。此外，数据库搜索和功能注释分析支持我们的新预测值得进一步的实验验证。
总之，我们的新方法 NetPredATC 通过整合药物-靶标网络和数据，可以更准确地预测药物的 ATC 编码，这将促进药物机制的理解和药物重新定位和发现。
NetPredATC 可在 http://doc.aporc.org/wiki/NetPredATC 获得。
ycwang@nwipb.cas.cn 或 ywang@amss.ac.cn
补充数据可在《生物信息学》在线获取。
