College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, China.
Bioinformatics. 2020 Jun 1;36(11):3568-3569. doi: 10.1093/bioinformatics/btaa166.
Anatomical therapeutic chemical (ATC) classification system is very important for drug utilization and studies. Correct prediction of the 14 classes in the first level for given drugs is an essential problem for the study on such system. Several multi-label classifiers have been proposed in this regard. However, only two of them provided the web servers and their performance was not very high. On the other hand, although some rest classifiers can provide better performance, they were built based on some prior knowledge on drugs, such as information of chemical-chemical interaction and chemical ontology, leading to limited applications. Furthermore, provided codes of these classifiers are almost inaccessible for pharmacologists.
In this study, we built a simple web server, namely iATC-FRAKEL. This web server only required the SMILES format of drugs as input and extracted their fingerprints for making prediction. The performance of the iATC-FRAKEL was much higher than all existing web servers and was comparable to the best multi-label classifier but had much wider applications. Such web server can be visited at http://cie.shmtu.edu.cn/iatc/index.
The web server is available at http://cie.shmtu.edu.cn/iatc/index.
Supplementary data are available at Bioinformatics online.
解剖治疗化学（ATC）分类系统对于药物利用和研究非常重要。正确预测给定药物第一级的 14 个类别是该系统研究的一个基本问题。在这方面已经提出了几种多标签分类器。然而，其中只有两个提供了网络服务器，其性能不是很高。另一方面，尽管一些剩余分类器可以提供更好的性能，但它们是基于药物的一些先验知识构建的，例如化学-化学相互作用和化学本体论的信息，导致应用受限。此外，这些分类器的提供的代码几乎无法访问给药理学家。
在本研究中，我们构建了一个简单的网络服务器，即 iATC-FRAKEL。该网络服务器仅需要药物的 SMILES 格式作为输入，并提取其指纹进行预测。iATC-FRAKEL 的性能明显高于所有现有的网络服务器，与最佳多标签分类器相当，但应用范围更广。该网络服务器可在 http://cie.shmtu.edu.cn/iatc/index 访问。
该网络服务器可在 http://cie.shmtu.edu.cn/iatc/index 访问。
补充数据可在生物信息学在线获得。
