{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T21:34:04Z","timestamp":1776202444453,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T00:00:00Z","timestamp":1616716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100017054","name":"NSFC-Zhejiang Joint Fund","doi-asserted-by":"publisher","award":["U1909208"],"award-info":[{"award-number":["U1909208"]}],"id":[{"id":"10.13039\/100017054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772552"],"award-info":[{"award-number":["61772552"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072473"],"award-info":[{"award-number":["62072473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"publisher","award":["B18059"],"award-info":[{"award-number":["B18059"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Provinvial Science and Technology Program","award":["2018WK4001"],"award-info":[{"award-number":["2018WK4001"]}]},{"name":"Hunan Provinvial Science and Technology Program","award":["2020GK2019"],"award-info":[{"award-number":["2020GK2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The Anatomical Therapeutic Chemical (ATC) system is an official classification system established by the World Health Organization for medicines. Correctly assigning ATC classes to given compounds is an important research problem in drug discovery, which can not only discover the possible active ingredients of the compounds, but also infer theirs therapeutic, pharmacological and chemical properties.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we develop an end-to-end multi-label classifier called CGATCPred to predict 14 main ATC classes for given compounds. In order to extract rich features of each compound, we use the deep Convolutional Neural Network and shortcut connections to represent and learn the seven association scores between the given compound and others. Moreover, we construct the correlation graph of ATC classes and then apply graph convolutional network on the graph for label embedding abstraction. We use all label embedding to guide the learning process of compound representation. As a result, by using the Jackknife test, CGATCPred obtain reliable Aiming of 81.94%, Coverage of 82.88%, Accuracy 80.81%, Absolute True 76.58% and Absolute False 2.75%, yielding significantly improvements compared to exiting multi-label classifiers.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The codes of CGATCPred are available at https:\/\/github.com\/zhc940702\/CGATCPred and https:\/\/zenodo.org\/record\/4552917.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab204","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T12:20:23Z","timestamp":1616588423000},"page":"2841-2847","source":"Crossref","is-referenced-by-count":26,"title":["A convolutional neural network and graph convolutional network-based method for predicting the classification of anatomical therapeutic chemicals"],"prefix":"10.1093","volume":"37","author":[{"given":"Haochen","family":"Zhao","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]},{"given":"Yaohang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Old Dominion University , Norfolk, VA 23529-0001, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}]}],"member":"286","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"key":"2023061310573732900_btab204-B1","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1093\/bioinformatics\/bty1035","article-title":"Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions","volume":"35","author":"Alazmi","year":"2019","journal-title":"Bioinformatics"},{"key":"2023061310573732900_btab204-B2","first-page":"186","article-title":"Utilization of the parenteral morphine in emergency department using the anatomical therapeutic chemical classification\/defined daily doses (ATC\/DDD) system","volume":"8","author":"Bozorgi","year":"2020","journal-title":"Bull. 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