{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T23:59:04Z","timestamp":1775087944348,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"S9","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100011512","name":"National Research Foundation","doi-asserted-by":"publisher","award":["NRF-2015M3A9C4075820"],"award-info":[{"award-number":["NRF-2015M3A9C4075820"]}],"id":[{"id":"10.13039\/100011512","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2022R1A2C1010731"],"award-info":[{"award-number":["NRF-2022R1A2C1010731"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2021R1A6A3A13046324"],"award-info":[{"award-number":["NRF-2021R1A6A3A13046324"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Ministry of Oceans and Fisheries, Korea","award":["20180384"],"award-info":[{"award-number":["20180384"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>G-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related diseases, including cancer and diabetes; at least a third of the marketed drugs target GPCRs. Thus, characterization of their signaling and regulatory mechanisms is crucial for the development of effective drugs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we developed a machine learning model to identify GPCR agonists and antagonists. We designed two-step prediction models: the first model identified the ligands binding to GPCRs and the second model classified the ligands as agonists or antagonists. Using 990 selected subset features from 5270 molecular descriptors calculated from 4590 ligands deposited in two drug databases, our model classified non-ligands, agonists, and antagonists of GPCRs, and achieved an area under the ROC curve (AUC) of 0.795, sensitivity of 0.716, specificity of 0.744, and accuracy of 0.733. In addition, we verified that 70% (44 out of 63) of FDA-approved GPCR-targeting drugs were correctly classified into their respective groups.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Studies of ligand\u2013GPCR interaction recognition are important for the characterization of drug action mechanisms. Our GPCR\u2013ligand interaction prediction model can be employed in the pharmaceutical sciences for the efficient virtual screening of putative GPCR-binding agonists and antagonists.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04877-7","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T13:02:53Z","timestamp":1660827773000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists"],"prefix":"10.1186","volume":"23","author":[{"given":"Jooseong","family":"Oh","sequence":"first","affiliation":[]},{"given":"Hyi-thaek","family":"Ceong","sequence":"additional","affiliation":[]},{"given":"Dokyun","family":"Na","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9545-6654","authenticated-orcid":false,"given":"Chungoo","family":"Park","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"issue":"7245","key":"4877_CR1","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1038\/nature08144","volume":"459","author":"DM Rosenbaum","year":"2009","unstructured":"Rosenbaum DM, Rasmussen SG, Kobilka BK. The structure and function of G-protein-coupled receptors. Nature. 2009;459(7245):356\u201363.","journal-title":"Nature"},{"issue":"3","key":"4877_CR2","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1080\/08923973.2018.1434792","volume":"40","author":"D Wang","year":"2018","unstructured":"Wang D. The essential role of G protein-coupled receptor (GPCR) signaling in regulating T cell immunity. Immunopharmacol Immunotoxicol. 2018;40(3):187\u201392.","journal-title":"Immunopharmacol Immunotoxicol"},{"issue":"1","key":"4877_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/nrd3859","volume":"12","author":"RC Stevens","year":"2013","unstructured":"Stevens RC, Cherezov V, Katritch V, Abagyan R, Kuhn P, Rosen H, Wuthrich K. The GPCR network: a large-scale collaboration to determine human GPCR structure and function. Nat Rev Drug Discov. 2013;12(1):25\u201334.","journal-title":"Nat Rev Drug Discov"},{"issue":"7436","key":"4877_CR4","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1038\/nature11896","volume":"494","author":"AJ Venkatakrishnan","year":"2013","unstructured":"Venkatakrishnan AJ, Deupi X, Lebon G, Tate CG, Schertler GF, Babu MM. Molecular signatures of G-protein-coupled receptors. Nature. 2013;494(7436):185\u201394.","journal-title":"Nature"},{"issue":"12","key":"4877_CR5","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1038\/nrd.2017.178","volume":"16","author":"AS Hauser","year":"2017","unstructured":"Hauser AS, Attwood MM, Rask-Andersen M, Schi\u00f6th HB, Gloriam DE. Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov. 2017;16(12):829\u201342.","journal-title":"Nat Rev Drug Discov"},{"issue":"9","key":"4877_CR6","doi-asserted-by":"publisher","first-page":"2235","DOI":"10.1021\/acs.jpclett.8b00633","volume":"9","author":"C Bushdid","year":"2018","unstructured":"Bushdid C, de March CA, Fiorucci S, Matsunami H, Golebiowski J. Agonists of G-protein-coupled odorant receptors are predicted from chemical features. J Phys Chem Lett. 2018;9(9):2235\u201340.","journal-title":"J Phys Chem Lett"},{"key":"4877_CR7","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.sbi.2019.03.022","volume":"55","author":"A Jabeen","year":"2019","unstructured":"Jabeen A, Ranganathan S. Applications of machine learning in GPCR bioactive ligand discovery. Curr Opin Struct Biol. 2019;55:66\u201376.","journal-title":"Curr Opin Struct Biol"},{"issue":"D1","key":"4877_CR8","doi-asserted-by":"publisher","first-page":"D1091","DOI":"10.1093\/nar\/gkx1121","volume":"46","author":"SD Harding","year":"2018","unstructured":"Harding SD, Sharman JL, Faccenda E, Southan C, Pawson AJ, Ireland S, Gray AJG, Bruce L, Alexander SPH, Anderton S, et al. The IUPHAR\/BPS guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY. Nucleic Acids Res. 2018;46(D1):D1091\u2013106.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"4877_CR9","doi-asserted-by":"publisher","first-page":"7519","DOI":"10.1038\/s41598-017-07448-6","volume":"7","author":"H Yu","year":"2017","unstructured":"Yu H, Jung J, Yoon S, Kwon M, Bae S, Yim S, Lee J, Kim S, Kang Y, Lee D. CODA: Integrating multi-level context-oriented directed associations for analysis of drug effects. Sci Rep. 2017;7(1):7519.","journal-title":"Sci Rep"},{"key":"4877_CR10","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/B978-0-12-409547-2.12345-5","volume-title":"Comprehensive medicinal chemistry III","author":"D Bajusz","year":"2017","unstructured":"Bajusz D, R\u00e1cz A, H\u00e9berger K. 3.14 - chemical data formats, fingerprints, and other molecular descriptions for database analysis and searching. In: Chackalamannil S, Rotella D, Ward SE, editors. Comprehensive medicinal chemistry III. Oxford: Elsevier; 2017. p. 329\u201378."},{"issue":"19","key":"4877_CR11","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1016\/j.drudis.2012.10.011","volume":"18","author":"T Kogej","year":"2013","unstructured":"Kogej T, Blomberg N, Greasley PJ, Mundt S, Vainio MJ, Schamberger J, Schmidt G, H\u00fcser J. Big pharma screening collections: more of the same or unique libraries? the AstraZeneca\u2013bayer pharma AG case. Drug Discov Today. 2013;18(19):1014\u201324.","journal-title":"Drug Discov Today"},{"key":"4877_CR12","first-page":"443","volume-title":"Data mining (Third Edition)","author":"J Han","year":"2012","unstructured":"Han J, Kamber M, Pei J. 10 - cluster analysis: basic concepts and methods. In: Han J, Kamber M, Pei J, editors. Data mining (Third Edition). Boston: Morgan Kaufmann; 2012. p. 443\u201395."},{"key":"4877_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2020.128313","volume":"262","author":"G Piir","year":"2021","unstructured":"Piir G, Sild S, Maran U. Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere. 2021;262: 128313.","journal-title":"Chemosphere"},{"key":"4877_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-43125-6","author":"L Li","year":"2019","unstructured":"Li L, Koh CC, Reker D, Brown JB, Wang H, Lee NK, Liow H-H, Dai H, Fan H-M, Chen L, et al. Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees. Sci Rep. 2019. https:\/\/doi.org\/10.1038\/s41598-019-43125-6.","journal-title":"Sci Rep"},{"issue":"1","key":"4877_CR15","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1093\/bib\/bbs006","volume":"14","author":"W-J Lin","year":"2013","unstructured":"Lin W-J, Chen JJ. Class-imbalanced classifiers for high-dimensional data. Brief Bioinform. 2013;14(1):13\u201326.","journal-title":"Brief Bioinform"},{"issue":"4","key":"4877_CR16","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1124\/mol.117.111062","volume":"93","author":"K Sriram","year":"2018","unstructured":"Sriram K, Insel PA. G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol Pharmacol. 2018;93(4):251.","journal-title":"Mol Pharmacol"},{"issue":"D1","key":"4877_CR17","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074\u201382.","journal-title":"Nucleic Acids Res"},{"key":"4877_CR18","first-page":"237","volume":"56","author":"A Mauri","year":"2006","unstructured":"Mauri A, Consonni V, Pavan M, Todeschini R. DRAGON software: an easy approach to molecular descriptor calculations. MATCH Commun Math Comput Chem. 2006;56:237\u201348.","journal-title":"MATCH Commun Math Comput Chem"},{"issue":"1","key":"4877_CR19","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13321-015-0069-3","volume":"7","author":"D Bajusz","year":"2015","unstructured":"Bajusz D, R\u00e1cz A, H\u00e9berger K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminf. 2015;7(1):20.","journal-title":"J Cheminf"},{"issue":"7","key":"4877_CR20","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1021\/ci800038f","volume":"48","author":"H Hong","year":"2008","unstructured":"Hong H, Xie Q, Ge W, Qian F, Fang H, Shi L, Su Z, Perkins R, Tong W. Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model. 2008;48(7):1337\u201344.","journal-title":"J Chem Inf Model"},{"key":"4877_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","volume":"36","author":"M Kursa","year":"2010","unstructured":"Kursa M, Rudnicki W. Feature selection with boruta package. J Stat Softw. 2010;36:1\u201313.","journal-title":"J Stat Softw"},{"key":"4877_CR22","unstructured":"Piotr Romanski LK, Patrick Schratz. FSelector: selecting attributes. R package version 033 2021:https:\/\/CRAN.R-project.org\/package=FSelector."},{"issue":"3","key":"4877_CR23","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2(3):18\u201322.","journal-title":"R News"},{"issue":"2","key":"4877_CR24","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","volume":"21","author":"T Fushiki","year":"2011","unstructured":"Fushiki T. Estimation of prediction error by using K-fold cross-validation. Stat Comput. 2011;21(2):137\u201346.","journal-title":"Stat Comput"},{"key":"4877_CR25","first-page":"667","volume-title":"Data mining and knowledge discovery handbook","author":"G Tsoumakas","year":"2010","unstructured":"Tsoumakas G, Katakis I, Vlahavas I. Mining multi-label data. In: Maimon O, Rokach L, editors. Data mining and knowledge discovery handbook. Boston: Springer; 2010. p. 667\u201385."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04877-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04877-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04877-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T13:03:03Z","timestamp":1660827783000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04877-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,18]]},"references-count":25,"journal-issue":{"issue":"S9","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["4877"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04877-7","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,18]]},"assertion":[{"value":"1 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"346"}}