Jian-Peng Zhou1, Lei Chen1,2, Tianyun Wang1, Min Liu1. 1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. 2. Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, China.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The web server is available at http://cie.shmtu.edu.cn/iatc/index. CONTACT: chen_lei1@163.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The web server is available at http://cie.shmtu.edu.cn/iatc/index. CONTACT: chen_lei1@163.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Lei Chen; Zhandong Li; Tao Zeng; Yu-Hang Zhang; KaiYan Feng; Tao Huang; Yu-Dong Cai Journal: Biomed Res Int Date: 2021-07-06 Impact factor: 3.411
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Authors: Min Li; XiaoYong Pan; Tao Zeng; Yu-Hang Zhang; Kaiyan Feng; Lei Chen; Tao Huang; Yu-Dong Cai Journal: Biomed Res Int Date: 2020-06-15 Impact factor: 3.411
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