{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:36:15Z","timestamp":1772908575024,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Discovering drug\u2019s 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\u2019s potential ATC-codes by computational approaches.<\/jats:p>\n               <jats:p>Results: Here, we introduce drug-target network to computationally predict drug\u2019s 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\u2019s potential ATC-codes by integrating chemical structures and target proteins. Specifically, we first construct a gold-standard positive dataset from drugs\u2019 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\u2019s 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\u2019s 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.<\/jats:p>\n               <jats:p>Conclusion: In conclusion, our new method, NetPredATC, can predict drug\u2019s ATC-codes more accurately by incorporating drug-target network and integrating data, which will promote drug mechanism understanding and drug repositioning and discovery.<\/jats:p>\n               <jats:p>Availability: NetPredATC is available at http:\/\/doc.aporc.org\/wiki\/NetPredATC.<\/jats:p>\n               <jats:p>Contact: \u00a0ycwang@nwipb.cas.cn or ywang@amss.ac.cn<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt158","type":"journal-article","created":{"date-parts":[[2013,4,6]],"date-time":"2013-04-06T04:19:30Z","timestamp":1365221970000},"page":"1317-1324","source":"Crossref","is-referenced-by-count":44,"title":["Network predicting drug\u2019s anatomical therapeutic chemical code"],"prefix":"10.1093","volume":"29","author":[{"given":"Yong-Cui","family":"Wang","sequence":"first","affiliation":[{"name":"1 Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China, 2College of Science, China Agricultural University, Beijing 100083, China, 3National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 100190, China and 4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Shi-Long","family":"Chen","sequence":"additional","affiliation":[{"name":"1 Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China, 2College of Science, China Agricultural University, Beijing 100083, China, 3National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 100190, China and 4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Nai-Yang","family":"Deng","sequence":"additional","affiliation":[{"name":"1 Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China, 2College of Science, China Agricultural University, Beijing 100083, China, 3National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 100190, China and 4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"1 Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China, 2College of Science, China Agricultural University, Beijing 100083, China, 3National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 100190, China and 4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"1 Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China, 2College of Science, China Agricultural University, Beijing 100083, China, 3National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing 100190, China and 4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"286","published-online":{"date-parts":[[2013,4,5]]},"reference":[{"key":"2023012810342476700_btt158-B27","doi-asserted-by":"crossref","first-page":"e22187","DOI":"10.1371\/journal.pone.0022187","article-title":"Network neighbors of drug targets contribute to drug side-effect similarity","volume":"6","author":"Brouwers","year":"2011","journal-title":"PLoS ONE"},{"key":"2023012810342476700_btt158-B2","doi-asserted-by":"crossref","DOI":"10.1145\/1031171.1031186","article-title":"Hierarchical document categorization with support vector machines","volume-title":"Proceedings of the 13th ACM International Conference on Information and Knowledge Management","author":"Cai","year":"2004"},{"key":"2023012810342476700_btt158-B28","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1126\/science.1158140","article-title":"Drug target identification using side-effect similarity","volume":"321","author":"Campillos","year":"2008","journal-title":"Science"},{"key":"2023012810342476700_btt158-B4","doi-asserted-by":"crossref","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: a library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"2023012810342476700_btt158-B6","doi-asserted-by":"crossref","first-page":"W55","DOI":"10.1093\/nar\/gkn307","article-title":"SuperPred: drug classification and target prediction","volume":"36","author":"Dunkel","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"2023012810342476700_btt158-B7","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/S0097-8485(96)80004-0","article-title":"Use of receiver operating characteristic (roc) analysis to evaluate sequence matching","volume":"20","author":"Gribskov","year":"1996","journal-title":"Comput. Chem."},{"key":"2023012810342476700_btt158-B8","doi-asserted-by":"crossref","first-page":"D919","DOI":"10.1093\/nar\/gkm862","article-title":"Supertarget and matador: resources for exploring drug-target relationships","volume":"36","author":"G\u00fcnther","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"2023012810342476700_btt158-B9","doi-asserted-by":"crossref","first-page":"11853","DOI":"10.1021\/ja036030u","article-title":"Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways","volume":"125","author":"Hattori","year":"2003","journal-title":"J. Am. Chem. Soc."},{"key":"2023012810342476700_btt158-B10","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1214\/009053607000000677","article-title":"Kernel methods in machine learning","volume":"36","author":"Hofmann","year":"2008","journal-title":"Ann. Stat."},{"key":"2023012810342476700_btt158-B11","doi-asserted-by":"crossref","first-page":"D354","DOI":"10.1093\/nar\/gkj102","article-title":"From genomics to chemical genomics: new developments in kegg","volume":"34","author":"Kanehisa","year":"2006","journal-title":"Nucleic Acids Res."},{"key":"2023012810342476700_btt158-B12","first-page":"296","article-title":"An information-theoretic definition of similarity","volume-title":"ICML 98: Proceedings of the Fifteenth International Conference on Machine Learning","author":"Lin","year":"1998"},{"key":"2023012810342476700_btt158-B13","first-page":"1601","article-title":"Kernel-based learning of hierarchical multilabel classification models","volume":"7","author":"Rousu","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"2023012810342476700_btt158-B14","doi-asserted-by":"crossref","first-page":"D431","DOI":"10.1093\/nar\/gkh081","article-title":"Brenda, the enzyme database: updates and major new developments","volume":"32","author":"Schomburg","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"2023012810342476700_btt158-B15","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4057.003.0005","volume-title":"Support Vector Machine Applications in Computational Biology","author":"Scholkopf","year":"2004"},{"key":"2023012810342476700_btt158-B16","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/0022-2836(81)90087-5","article-title":"Identification of common molecular subsequences","volume":"147","author":"Smith","year":"1981","journal-title":"J. Mol. Biol."},{"key":"2023012810342476700_btt158-B17","doi-asserted-by":"crossref","first-page":"370","DOI":"10.2174\/157018010791163433","article-title":"Computationally probing drug-protein interactions via support vector machine","volume":"7","author":"Wang","year":"2010","journal-title":"Lett. Drug Des. Discov."},{"key":"2023012810342476700_btt158-B18","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.compbiolchem.2011.10.003","article-title":"Kernel based data fusion improves the drug-protein interaction prediction","volume":"35","author":"Wang","year":"2011","journal-title":"Comput. Biol. Chem."},{"key":"2023012810342476700_btt158-B19","article-title":"The selection and use of essential medicines","author":"WHO Expert Committee","year":"2006"},{"key":"2023012810342476700_btt158-B20","doi-asserted-by":"crossref","first-page":"D901","DOI":"10.1093\/nar\/gkm958","article-title":"Drugbank: a knowledgebase for drugs, drug actions and drug targets","volume":"36","author":"Wishart","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"2023012810342476700_btt158-B21","article-title":"Class-boundary alignment for imbalanced dataset learning","volume-title":"Workshop on Learning from Imbalanced Datasets II","author":"Wu","year":"2003"},{"key":"2023012810342476700_btt158-B22","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1038\/msb.2008.27","article-title":"Network-based global inference of human disease genes","volume":"4","author":"Wu","year":"2008","journal-title":"Mol. Syst. Biol."},{"key":"2023012810342476700_btt158-B23","doi-asserted-by":"crossref","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","article-title":"Prediction of drug-target interaction networks from the integration of chemical and genomic spaces","volume":"24","author":"Yamanishi","year":"2008","journal-title":"Bioinformatics"},{"key":"2023012810342476700_btt158-B24","doi-asserted-by":"crossref","first-page":"i246","DOI":"10.1093\/bioinformatics\/btq176","article-title":"Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework","volume":"26","author":"Yamanishi","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012810342476700_btt158-B26","doi-asserted-by":"crossref","first-page":"e11764","DOI":"10.1371\/journal.pone.0011764","article-title":"Network-based relating pharmacological and genomic spaces for drug target identification","volume":"5","author":"Zhao","year":"2010","journal-title":"PLoS ONE"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/29\/10\/1317\/48886693\/bioinformatics_29_10_1317.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/29\/10\/1317\/48886693\/bioinformatics_29_10_1317.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T12:20:22Z","timestamp":1674908422000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/29\/10\/1317\/260431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,4,5]]},"references-count":24,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2013,5,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btt158","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2013,5,15]]},"published":{"date-parts":[[2013,4,5]]}}}