{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:40:15Z","timestamp":1773218415686,"version":"3.50.1"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:00:00Z","timestamp":1584403200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Berlin-Brandenburg Research Platform BB3R"},{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Education and Research","doi-asserted-by":"publisher","award":["031A262C"],"award-info":[{"award-number":["031A262C"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012353","name":"DKTK","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012353","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Charit\u00e9\u2014University Medicine Berlin"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug\u2013drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs\u2014published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http:\/\/insilico-cyp.charite.de\/SuperCYPsPred\/). The web server does not require log in or registration and is free to use.<\/jats:p>","DOI":"10.1093\/nar\/gkaa166","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T12:24:25Z","timestamp":1583497465000},"page":"W580-W585","source":"Crossref","is-referenced-by-count":84,"title":["SuperCYPsPred\u2014a web server for the prediction of cytochrome activity"],"prefix":"10.1093","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8072-5594","authenticated-orcid":false,"given":"Priyanka","family":"Banerjee","sequence":"first","affiliation":[{"name":"Structural Bioinformatics Group, Institute for Physiology & ECRC, Charit\u00e9,\u00a0University Medicine Berlin, 10115\u00a0Berlin, Germany"}]},{"given":"Mathias","family":"Dunkel","sequence":"first","affiliation":[{"name":"Structural Bioinformatics Group, Institute for Physiology & ECRC, Charit\u00e9,\u00a0University Medicine Berlin, 10115\u00a0Berlin, Germany"}]},{"given":"Emanuel","family":"Kemmler","sequence":"first","affiliation":[{"name":"Structural Bioinformatics Group, Institute for Physiology & ECRC, Charit\u00e9,\u00a0University Medicine Berlin, 10115\u00a0Berlin, Germany"}]},{"given":"Robert","family":"Preissner","sequence":"first","affiliation":[{"name":"Structural Bioinformatics Group, Institute for Physiology & ECRC, Charit\u00e9,\u00a0University Medicine Berlin, 10115\u00a0Berlin, Germany"}]}],"member":"286","published-online":{"date-parts":[[2020,3,17]]},"reference":[{"key":"2020062614035831900_B1","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1111\/j.1365-2125.2004.02194.x","article-title":"The use of pharmacokinetic and pharmacodynamic data in the assessment of drug safety in early drug development","volume":"58","author":"Walker","year":"2004","journal-title":"Br. J. Clin. Pharmacol."},{"key":"2020062614035831900_B2","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1002\/jcph.23","article-title":"Drug-induced liver injury: the role of drug metabolism and transport","volume":"53","author":"Corsini","year":"2013","journal-title":"J. Clin.Pharmacol."},{"key":"2020062614035831900_B3","first-page":"2","article-title":"The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects","volume":"76","author":"Lynch","year":"2007","journal-title":"Am. Fam. Physician"},{"key":"2020062614035831900_B4","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1001\/jama.279.15.1200","article-title":"Incidence of adverse drug reactions in hospitalized patients a meta-analysis of prospective studies","volume":"279","author":"Lazarou","year":"1998","journal-title":"JAMA"},{"key":"2020062614035831900_B5","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.dmpk.2019.11.006","article-title":"Computational prediction of cytochrome P450 inhibition and induction","volume":"35","author":"Kato","year":"2019","journal-title":"Drug Metab. Pharmacokinet."},{"key":"2020062614035831900_B6","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.tips.2019.07.005","article-title":"Artificial intelligence for drug toxicity and safety","volume":"40","author":"Basile","year":"2019","journal-title":"Trends Pharmacol. Sci."},{"key":"2020062614035831900_B7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fchem.2018.00362","article-title":"Prediction is a balancing Act: importance of sampling methods to balance sensitivity and specificity of predictive models based on imbalanced chemical data sets","volume":"6","author":"Banerjee","year":"2018","journal-title":"Front. Chem."},{"key":"2020062614035831900_B8","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1093\/nar\/gky318","article-title":"ProTox-II: a webserver for the prediction of toxicity of chemicals","volume":"46","author":"Banerjee","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"2020062614035831900_B9","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/cpt.1596","article-title":"Quantitative prediction of CYP3A4- and CYP3A5-Mediated drug interactions","volume":"107","author":"Guo","year":"2020","journal-title":"Clin. Pharmacol. Ther."},{"key":"2020062614035831900_B10","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1080\/1062936X.2017.1399925","article-title":"In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method","volume":"28","author":"Lee","year":"2017","journal-title":"SAR QSAR Environ. Res."},{"key":"2020062614035831900_B11","doi-asserted-by":"crossref","first-page":"1-14","DOI":"10.3389\/fphar.2017.00889","article-title":"vNN web server for ADMET predictions","volume":"8","author":"Schyman","year":"2017","journal-title":"Front. Pharmacol."},{"key":"2020062614035831900_B12","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1093\/bioinformatics\/bty707","article-title":"admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties","volume":"35","author":"Yang","year":"2019","journal-title":"Bioinformatics"},{"key":"2020062614035831900_B13","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1093\/bioinformatics\/btt325","article-title":"WhichCyp: prediction of cytochromes P450 inhibition","volume":"29","author":"Rostkowski","year":"2013","journal-title":"Bioinformatics"},{"key":"2020062614035831900_B14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-016-0162-2","article-title":"Computational methods for prediction of in vitro effects of new chemical structures","volume":"8","author":"Banerjee","year":"2016","journal-title":"J. Cheminform."},{"key":"2020062614035831900_B15","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1038\/nbt.1581","article-title":"Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries","volume":"27","author":"Veith","year":"2009","journal-title":"Nat. Biotechnol."},{"key":"2020062614035831900_B16","doi-asserted-by":"crossref","first-page":"D237","DOI":"10.1093\/nar\/gkp970","article-title":"SuperCYP: a comprehensive database on Cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions","volume":"38","author":"Preissner","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"2020062614035831900_B17","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0720-048X(97)00157-5","article-title":"Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology","volume":"27","author":"van\u00a0Erkel","year":"1998","journal-title":"Eur. J. Radiol."},{"key":"2020062614035831900_B18","doi-asserted-by":"crossref","first-page":"4336","DOI":"10.1021\/acs.molpharmaceut.8b00110","article-title":"Prediction of human cytochrome P450 inhibition using a multitask deep autoencoder neural network","volume":"15","author":"Li","year":"2018","journal-title":"Mol. Pharm."},{"key":"2020062614035831900_B19","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jbiotec.2017.07.028","article-title":"KNIME for reproducible cross-domain analysis of life science data","volume":"261","author":"Fillbrunn","year":"2017","journal-title":"J. Biotechnol."},{"key":"2020062614035831900_B20","doi-asserted-by":"crossref","first-page":"377","DOI":"10.2165\/00002512-200219050-00006","article-title":"Sertraline a review of its use in the management of major depressive disorder in elderly patients","volume":"19","author":"Muijsers","year":"2002","journal-title":"Drugs Aging"},{"key":"2020062614035831900_B21","first-page":"511","article-title":"The cancer drug fraction of metabolism database","volume":"8","author":"Hua","year":"2019","journal-title":"CPT: Pharmacometrics Syst. Pharmacol."}],"container-title":["Nucleic Acids Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/nar\/article-pdf\/48\/W1\/W580\/33432872\/gkaa166.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/nar\/article-pdf\/48\/W1\/W580\/33432872\/gkaa166.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T06:20:19Z","timestamp":1593238819000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/nar\/article\/48\/W1\/W580\/5809167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,17]]},"references-count":21,"journal-issue":{"issue":"W1","published-online":{"date-parts":[[2020,3,17]]},"published-print":{"date-parts":[[2020,7,2]]}},"URL":"https:\/\/doi.org\/10.1093\/nar\/gkaa166","relation":{},"ISSN":["0305-1048","1362-4962"],"issn-type":[{"value":"0305-1048","type":"print"},{"value":"1362-4962","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,7,2]]},"published":{"date-parts":[[2020,3,17]]}}}