{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:35:07Z","timestamp":1760574907069,"version":"build-2065373602"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-01097-y","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:05:08Z","timestamp":1760522708000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of UGT-mediated phase II metabolism via ligand- and structure-based predictive models"],"prefix":"10.1186","volume":"17","author":[{"given":"Ludovica","family":"Bono","sequence":"first","affiliation":[]},{"given":"Filippo","family":"Lunghini","sequence":"additional","affiliation":[]},{"given":"Emanuela","family":"Sabato","sequence":"additional","affiliation":[]},{"given":"Akash Deep","family":"Biswas","sequence":"additional","affiliation":[]},{"given":"Angelica","family":"Mazzolari","sequence":"additional","affiliation":[]},{"given":"Alessandro","family":"Pedretti","sequence":"additional","affiliation":[]},{"given":"Andrea R.","family":"Beccari","sequence":"additional","affiliation":[]},{"given":"Giulio","family":"Vistoli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6092-5011","authenticated-orcid":false,"given":"Serena","family":"Vittorio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"1097_CR1","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.compbiomed.2019.01.008","volume":"106","author":"SR Kazmi","year":"2019","unstructured":"Kazmi SR, Jun R, Yu M-S et al (2019) In silico approaches and tools for the prediction of drug metabolism and fate: a review. Comput Biol Med 106:54\u201364. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.01.008","journal-title":"Comput Biol Med"},{"key":"1097_CR2","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.tips.2023.11.001","volume":"45","author":"B Dudas","year":"2024","unstructured":"Dudas B, Miteva MA (2024) Computational and artificial intelligence-based approaches for drug metabolism and transport prediction. Trends Pharmacol Sci 45:39\u201355. https:\/\/doi.org\/10.1016\/j.tips.2023.11.001","journal-title":"Trends Pharmacol Sci"},{"key":"1097_CR3","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1080\/17425255.2024.2330666","volume":"20","author":"A Paliwal","year":"2024","unstructured":"Paliwal A, Jain S, Kumar S et al (2024) Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine. Expert Opin Drug Metab Toxicol 20:181\u2013195. https:\/\/doi.org\/10.1080\/17425255.2024.2330666","journal-title":"Expert Opin Drug Metab Toxicol"},{"key":"1097_CR4","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1021\/acs.jmedchem.7b01473","volume":"61","author":"A Pedretti","year":"2018","unstructured":"Pedretti A, Mazzolari A, Vistoli G, Testa B (2018) MetaQSAR: an integrated database engine to manage and analyze metabolic data. J Med Chem 61:1019\u20131030. https:\/\/doi.org\/10.1021\/acs.jmedchem.7b01473","journal-title":"J Med Chem"},{"key":"1097_CR5","doi-asserted-by":"publisher","first-page":"5857","DOI":"10.3390\/molecules26195857","volume":"26","author":"A Mazzolari","year":"2021","unstructured":"Mazzolari A, Scaccabarozzi A, Vistoli G, Pedretti A (2021) Metaclass, a comprehensive classification system for predicting the occurrence of metabolic reactions based on the MetaQSAR database. Molecules 26:5857. https:\/\/doi.org\/10.3390\/molecules26195857","journal-title":"Molecules"},{"key":"1097_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/ijms241311064","author":"A Mazzolari","year":"2023","unstructured":"Mazzolari A, Perazzoni P, Sabato E et al (2023) Metaspot: a general approach for recognizing the reactive atoms undergoing metabolic reactions based on the MetaQSAR database. Int J Mol Sci. https:\/\/doi.org\/10.3390\/ijms241311064","journal-title":"Int J Mol Sci"},{"key":"1097_CR7","doi-asserted-by":"publisher","first-page":"3400","DOI":"10.1021\/acs.jcim.9b00376","volume":"59","author":"M \u0160\u00edcho","year":"2019","unstructured":"\u0160\u00edcho M, Stork C, Mazzolari A et al (2019) FAME 3: predicting the sites of metabolism in synthetic compounds and natural products for phase 1 and phase 2 metabolic enzymes. J Chem Inf Model 59:3400\u20133412. https:\/\/doi.org\/10.1021\/acs.jcim.9b00376","journal-title":"J Chem Inf Model"},{"key":"1097_CR8","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1021\/acs.jcim.3c01588","volume":"64","author":"Y Chen","year":"2024","unstructured":"Chen Y, Seidel T, Jacob RA et al (2024) Active learning approach for guiding site-of-metabolism measurement and annotation. J Chem Inf Model 64:348\u2013358. https:\/\/doi.org\/10.1021\/acs.jcim.3c01588","journal-title":"J Chem Inf Model"},{"issue":"4","key":"1097_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/pharmaceutics15041260","volume":"15","author":"TTV Tran","year":"2023","unstructured":"Tran TTV, Tayara H, Chong KT (2023) Artificial intelligence in drug metabolism and excretion prediction: recent advances, challenges, and future perspectives. Pharmaceutics 15(4):1260. https:\/\/doi.org\/10.3390\/pharmaceutics15041260","journal-title":"Pharmaceutics"},{"key":"1097_CR10","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0169-409X(02)00009-1","volume":"54","author":"MJ De Groot","year":"2002","unstructured":"De Groot MJ, Ekins S (2002) Pharmacophore modeling of cytochromes P450. Adv Drug Deliv Rev 54:367\u2013383. https:\/\/doi.org\/10.1016\/S0169-409X(02)00009-1","journal-title":"Adv Drug Deliv Rev"},{"key":"1097_CR11","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1124\/mol.65.2.301","volume":"65","author":"MJ Sorich","year":"2004","unstructured":"Sorich MJ, Miners JO, McKinnon RA, Smith PA (2004) Multiple pharmacophores for the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol Pharmacol 65:301\u2013308. https:\/\/doi.org\/10.1124\/mol.65.2.301","journal-title":"Mol Pharmacol"},{"key":"1097_CR12","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1038\/nrd4581","volume":"14","author":"J Kirchmair","year":"2015","unstructured":"Kirchmair J, G\u00f6ller AH, Lang D et al (2015) Predicting drug metabolism: experiment and\/or computation? Nat Rev Drug Discov 14:387\u2013404. https:\/\/doi.org\/10.1038\/nrd4581","journal-title":"Nat Rev Drug Discov"},{"key":"1097_CR13","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1007\/s11095-014-1511-3","volume":"32","author":"LJ Kingsley","year":"2015","unstructured":"Kingsley LJ, Wilson GL, Essex ME, Lill MA (2015) Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates. Pharm Res 32:986\u20131001. https:\/\/doi.org\/10.1007\/s11095-014-1511-3","journal-title":"Pharm Res"},{"key":"1097_CR14","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1021\/ci2000488","volume":"51","author":"J Zaretzki","year":"2011","unstructured":"Zaretzki J, Bergeron C, Rydberg P et al (2011) RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4. J Chem Inf Model 51:1667\u20131689. https:\/\/doi.org\/10.1021\/ci2000488","journal-title":"J Chem Inf Model"},{"key":"1097_CR15","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1039\/C5MB00118H","volume":"11","author":"G Mukherjee","year":"2015","unstructured":"Mukherjee G, Lal Gupta P, Jayaram B (2015) Predicting the binding modes and sites of metabolism of xenobiotics. Mol Biosyst 11:1914\u20131924. https:\/\/doi.org\/10.1039\/C5MB00118H","journal-title":"Mol Biosyst"},{"key":"1097_CR16","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1016\/j.biocel.2013.02.019","volume":"45","author":"A Rowland","year":"2013","unstructured":"Rowland A, Miners JO, Mackenzie PI (2013) The UDP-glucuronosyltransferases: Their role in drug metabolism and detoxification. Int J Biochem Cell Biol 45:1121\u20131132. https:\/\/doi.org\/10.1016\/j.biocel.2013.02.019","journal-title":"Int J Biochem Cell Biol"},{"key":"1097_CR17","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1152\/physrev.00058.2017","volume":"99","author":"R Meech","year":"2019","unstructured":"Meech R, Hu DG, McKinnon RA et al (2019) The UDP-glycosyltransferase (UGT) superfamily: new members, new functions, and novel paradigms. Physiol Rev 99:1153\u20131222. https:\/\/doi.org\/10.1152\/physrev.00058.2017","journal-title":"Physiol Rev"},{"key":"1097_CR18","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1021\/acsmedchemlett.8b00603","volume":"10","author":"A Mazzolari","year":"2019","unstructured":"Mazzolari A, Afzal AM, Pedretti A et al (2019) Prediction of UGT-mediated metabolism using the manually curated MetaQSAR database. ACS Med Chem Lett 10:633\u2013638. https:\/\/doi.org\/10.1021\/acsmedchemlett.8b00603","journal-title":"ACS Med Chem Lett"},{"key":"1097_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-022-00626-3","volume":"14","author":"M Huang","year":"2022","unstructured":"Huang M, Lou C, Wu Z et al (2022) In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network. J Cheminform 14:1\u201316. https:\/\/doi.org\/10.1186\/s13321-022-00626-3","journal-title":"J Cheminform"},{"key":"1097_CR20","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1021\/acs.jcim.8b00851","volume":"59","author":"Y Cai","year":"2019","unstructured":"Cai Y, Yang H, Li W et al (2019) Computational prediction of site of metabolism for UGT-catalyzed reactions. J Chem Inf Model 59:1085\u20131095. https:\/\/doi.org\/10.1021\/acs.jcim.8b00851","journal-title":"J Chem Inf Model"},{"key":"1097_CR21","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.jmb.2007.03.066","volume":"369","author":"MJ Miley","year":"2007","unstructured":"Miley MJ, Zielinska AK, Keenan JE et al (2007) Crystal structure of the cofactor-binding domain of the human phase II drug-metabolism enzyme UDP-glucuronosyltransferase 2B7. J Mol Biol 369:498\u2013511. https:\/\/doi.org\/10.1016\/j.jmb.2007.03.066","journal-title":"J Mol Biol"},{"key":"1097_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bcp.2019.113753","volume":"172","author":"L Zhang","year":"2020","unstructured":"Zhang L, Zhu L, Qu W et al (2020) Insight into tartrate inhibition patterns in vitro and in vivo based on cocrystal structure with UDP-glucuronosyltransferase 2B15. Biochem Pharmacol 172:113753. https:\/\/doi.org\/10.1016\/j.bcp.2019.113753","journal-title":"Biochem Pharmacol"},{"key":"1097_CR23","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1038\/sj.emboj.7600970","volume":"25","author":"W Offen","year":"2006","unstructured":"Offen W, Martinez-Fleites C, Yang M et al (2006) Structure of a flavonoid glucosyltransferase reveals the basis for plant natural product modification. EMBO J 25:1396\u20131405. https:\/\/doi.org\/10.1038\/sj.emboj.7600970","journal-title":"EMBO J"},{"key":"1097_CR24","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583\u2013589. https:\/\/doi.org\/10.1038\/s41586-021-03819-2","journal-title":"Nature"},{"key":"1097_CR25","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/s41392-023-01381-z","volume":"8","author":"Z Yang","year":"2023","unstructured":"Yang Z, Zeng X, Zhao Y, Chen R (2023) AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct Target Ther 8:115. https:\/\/doi.org\/10.1038\/s41392-023-01381-z","journal-title":"Signal Transduct Target Ther"},{"key":"1097_CR26","doi-asserted-by":"publisher","first-page":"W537","DOI":"10.1093\/nar\/gks375","volume":"40","author":"R Anandakrishnan","year":"2012","unstructured":"Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res 40:W537\u2013W541. https:\/\/doi.org\/10.1093\/nar\/gks375","journal-title":"Nucleic Acids Res"},{"key":"1097_CR27","doi-asserted-by":"crossref","unstructured":"Korb O, St\u00fctzle T, Exner TE (2006) PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design. pp 247\u2013258","DOI":"10.1007\/11839088_22"},{"key":"1097_CR28","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1093\/bioinformatics\/btaa774","volume":"37","author":"A Pedretti","year":"2021","unstructured":"Pedretti A, Mazzolari A, Gervasoni S et al (2021) The VEGA suite of programs: an versatile platform for cheminformatics and drug design projects. Bioinformatics 37:1174\u20131175. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa774","journal-title":"Bioinformatics"},{"key":"1097_CR29","doi-asserted-by":"publisher","first-page":"1668","DOI":"10.1002\/jcc.20290","volume":"26","author":"DA Case","year":"2005","unstructured":"Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668\u20131688. https:\/\/doi.org\/10.1002\/jcc.20290","journal-title":"J Comput Chem"},{"key":"1097_CR30","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1002\/jcc.20035","volume":"25","author":"J Wang","year":"2004","unstructured":"Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general amber force field. J Comput Chem 25:1157\u20131174. https:\/\/doi.org\/10.1002\/jcc.20035","journal-title":"J Comput Chem"},{"key":"1097_CR31","doi-asserted-by":"publisher","first-page":"3696","DOI":"10.1021\/acs.jctc.5b00255","volume":"11","author":"JA Maier","year":"2015","unstructured":"Maier JA, Martinez C, Kasavajhala K et al (2015) Ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696\u20133713. https:\/\/doi.org\/10.1021\/acs.jctc.5b00255","journal-title":"J Chem Theory Comput"},{"key":"1097_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2022.123824","volume":"252","author":"G Baron","year":"2023","unstructured":"Baron G, Borella S, della Vedova L et al (2023) An integrated metabolomic and proteomic approach for the identification of covalent inhibitors of the main protease (Mpro) of SARS-COV-2 from crude natural extracts. Talanta 252:123824. https:\/\/doi.org\/10.1016\/j.talanta.2022.123824","journal-title":"Talanta"},{"key":"1097_CR33","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.1021\/ct400341p","volume":"9","author":"DR Roe","year":"2013","unstructured":"Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9:3084\u20133095. https:\/\/doi.org\/10.1021\/ct400341p","journal-title":"J Chem Theory Comput"},{"key":"1097_CR34","doi-asserted-by":"publisher","first-page":"3314","DOI":"10.1021\/ct300418h","volume":"8","author":"BR Miller","year":"2012","unstructured":"Miller BR, McGee TD, Swails JM et al (2012) MMPBSA.py\u202f: an efficient program for end-state free energy calculations. J Chem Theory Comput 8:3314\u20133321. https:\/\/doi.org\/10.1021\/ct300418h","journal-title":"J Chem Theory Comput"},{"key":"1097_CR35","unstructured":"Stewart, James J.P. Stewart Computational Chemistry, Colorado Springs, CO U (2016) MOPAC2016"},{"key":"1097_CR36","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1093\/bioinformatics\/bty757","volume":"35","author":"M W\u00f3jcikowski","year":"2019","unstructured":"W\u00f3jcikowski M, Kukie\u0142ka M, Stepniewska-Dziubinska MM, Siedlecki P (2019) Development of a protein\u2013ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions. Bioinformatics 35:1334\u20131341. https:\/\/doi.org\/10.1093\/bioinformatics\/bty757","journal-title":"Bioinformatics"},{"key":"1097_CR37","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.bmc.2009.10.052","volume":"18","author":"G Vistoli","year":"2010","unstructured":"Vistoli G, Pedretti A, Mazzolari A, Testa B (2010) In silico prediction of human carboxylesterase-1 (hCES1) metabolism combining docking analyses and MD simulations. Bioorg Med Chem 18:320\u2013329. https:\/\/doi.org\/10.1016\/j.bmc.2009.10.052","journal-title":"Bioorg Med Chem"},{"key":"1097_CR38","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1002\/minf.201501030","volume":"35","author":"A Pedretti","year":"2016","unstructured":"Pedretti A, Granito C, Mazzolari A, Vistoli G (2016) Structural effects of some relevant missense mutations on the MECP2-DNA binding: a MD study analyzed by Rescore+, a versatile rescoring tool of the VEGA ZZ program. Mol Inform 35:424\u2013433. https:\/\/doi.org\/10.1002\/minf.201501030","journal-title":"Mol Inform"},{"key":"1097_CR39","doi-asserted-by":"publisher","first-page":"995","DOI":"10.4155\/fmc.11.54","volume":"3","author":"G Vistoli","year":"2011","unstructured":"Vistoli G, Pedretti A, Testa B (2011) Chemodiversity and molecular plasticity: recognition processes as explored by property spaces. Future Med Chem 3:995\u20131010. https:\/\/doi.org\/10.4155\/fmc.11.54","journal-title":"Future Med Chem"},{"key":"1097_CR40","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1021\/acs.jcim.7b00121","volume":"57","author":"G Vistoli","year":"2017","unstructured":"Vistoli G, Mazzolari A, Testa B, Pedretti A (2017) Binding space concept: a new approach to enhance the reliability of docking scores and its application to predicting butyrylcholinesterase hydrolytic activity. J Chem Inf Model 57:1691\u20131702. https:\/\/doi.org\/10.1021\/acs.jcim.7b00121","journal-title":"J Chem Inf Model"},{"key":"1097_CR41","doi-asserted-by":"crossref","unstructured":"Vistoli G, Talarico C, Vittorio S, et al (2025) Approaching Pharmacological Space: Events and Components. pp 151\u2013169","DOI":"10.1007\/978-1-0716-4003-6_7"},{"key":"1097_CR42","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1145\/1656274.1656280","volume":"11","author":"MR Berthold","year":"2009","unstructured":"Berthold MR, Cebron N, Dill F et al (2009) Knime - the Konstanz information miner. ACM SIGKDD Explor Newsl 11:26\u201331. https:\/\/doi.org\/10.1145\/1656274.1656280","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"1097_CR43","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0220-4","volume":"9","author":"EL Willighagen","year":"2017","unstructured":"Willighagen EL, Mayfield JW, Alvarsson J et al (2017) The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 9:33. https:\/\/doi.org\/10.1186\/s13321-017-0220-4","journal-title":"J Cheminform"},{"key":"1097_CR44","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/0014-5793(94)00453-6","volume":"346","author":"E Battaglia","year":"1994","unstructured":"Battaglia E et al (1994) The chemical modification of human liver UDP-glucuronosyltransferase UGT1*6 reveals the involvement of a carboxyl group in catalysis. FEBS Lett 346:146\u2013150. https:\/\/doi.org\/10.1016\/0014-5793(94)00453-6","journal-title":"FEBS Lett"},{"key":"1097_CR45","doi-asserted-by":"publisher","first-page":"36514","DOI":"10.1074\/jbc.M703107200","volume":"282","author":"D Li","year":"2007","unstructured":"Li D, Fournel-Gigleux S, Barr\u00e9 L et al (2007) Identification of aspartic acid and histidine residues mediating the reaction mechanism and the substrate specificity of the human UDP-glucuronosyltransferases 1A. J Biol Chem 282:36514\u201336524. https:\/\/doi.org\/10.1074\/jbc.M703107200","journal-title":"J Biol Chem"},{"key":"1097_CR46","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1093\/protein\/gzn030","volume":"21","author":"A-S Patana","year":"2008","unstructured":"Patana A-S, Kurkela M, Finel M, Goldman A (2008) Mutation analysis in UGT1A9 suggests a relationship between substrate and catalytic residues in UDP-glucuronosyltransferases. Protein Eng Des Sel 21:537\u2013543. https:\/\/doi.org\/10.1093\/protein\/gzn030","journal-title":"Protein Eng Des Sel"},{"key":"1097_CR47","doi-asserted-by":"publisher","first-page":"335","DOI":"10.3109\/03602532.2015.1071835","volume":"47","author":"PC Nair","year":"2015","unstructured":"Nair PC, Meech R, Mackenzie PI et al (2015) Insights into the UDP-sugar selectivities of human UDP-glycosyltransferases (UGT): a molecular modeling perspective. Drug Metab Rev 47:335\u2013345. https:\/\/doi.org\/10.3109\/03602532.2015.1071835","journal-title":"Drug Metab Rev"},{"key":"1097_CR48","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1124\/dmd.104.002667","volume":"33","author":"H Kaji","year":"2005","unstructured":"Kaji H, Kume T (2005) Regioselective glucuronidation of denopamine: marked species differences and identification of human UDP-glucuronosyltransferase isoform. Drug Metab Dispos 33:403\u2013412. https:\/\/doi.org\/10.1124\/dmd.104.002667","journal-title":"Drug Metab Dispos"},{"key":"1097_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphar.2023.1148670","volume":"14","author":"S Vittorio","year":"2023","unstructured":"Vittorio S, Lunghini F, Pedretti A et al (2023) Ensemble of structure and ligand-based classification models for hERG liability profiling. Front Pharmacol 14:1\u201316. https:\/\/doi.org\/10.3389\/fphar.2023.1148670","journal-title":"Front Pharmacol"},{"key":"1097_CR50","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1124\/dmd.105.007633","volume":"34","author":"BW Ogilvie","year":"2006","unstructured":"Ogilvie BW, Zhang D, Li W et al (2006) Glucuronidation converts gemfibrozil to a potent, metabolism-dependent inhibitor of CYP2C8: implications for drug-drug interactions. Drug Metab Dispos 34:191\u2013197. https:\/\/doi.org\/10.1124\/dmd.105.007633","journal-title":"Drug Metab Dispos"},{"key":"1097_CR51","doi-asserted-by":"publisher","first-page":"2040","DOI":"10.1124\/dmd.107.017269","volume":"35","author":"Y Mano","year":"2007","unstructured":"Mano Y, Usui T, Kamimura H (2007) The UDP-glucuronosyltransferase 2B7 isozyme is responsible for gemfibrozil glucuronidation in the human liver. Drug Metab Dispos 35:2040\u20132044. https:\/\/doi.org\/10.1124\/dmd.107.017269","journal-title":"Drug Metab Dispos"},{"key":"1097_CR52","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1080\/00498250701620734","volume":"37","author":"M Chen","year":"2007","unstructured":"Chen M, Howe D, Leduc B et al (2007) Identification and characterization of two chloramphenicol glucuronides from the in vitro glucuronidation of chloramphenicol in human liver microsomes. Xenobiotica 37:954\u2013971. https:\/\/doi.org\/10.1080\/00498250701620734","journal-title":"Xenobiotica"},{"key":"1097_CR53","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1124\/dmd.109.029900","volume":"38","author":"M Chen","year":"2010","unstructured":"Chen M, LeDuc B, Kerr S et al (2010) Identification of human UGT2B7 as the major isoform involved in the O -glucuronidation of chloramphenicol. Drug Metab Dispos 38:368\u2013375. https:\/\/doi.org\/10.1124\/dmd.109.029900","journal-title":"Drug Metab Dispos"},{"key":"1097_CR54","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1124\/dmd.30.11.1257","volume":"30","author":"MH Court","year":"2002","unstructured":"Court MH, Duan SX, Guillemette C et al (2002) Stereoselective conjugation of oxazepam by human UDP-glucuronosyltransferases (UGTs): S -oxazepam is glucuronidated by UGT2B15, while R -oxazepam is glucuronidated by UGT2B7 and UGT1A9. Drug Metab Dispos 30:1257\u20131265. https:\/\/doi.org\/10.1124\/dmd.30.11.1257","journal-title":"Drug Metab Dispos"},{"key":"1097_CR55","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/ph12010011","volume":"12","author":"B Salehi","year":"2019","unstructured":"Salehi B, Fokou PVT, Sharifi-Rad M et al (2019) The therapeutic potential of Naringenin: a review of clinical trials. Pharmaceuticals 12:11. https:\/\/doi.org\/10.3390\/ph12010011","journal-title":"Pharmaceuticals"},{"key":"1097_CR56","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1016\/S0090-9556(25)07570-1","volume":"22","author":"MD Green","year":"1994","unstructured":"Green MD, Oturu EM, Tephly TR (1994) Stable expression of a human liver UDP-glucuronosyltransferase (UGT2B15) with activity toward steroid and xenobiotic substrates. Drug Metab Dispos 22:799\u2013805","journal-title":"Drug Metab Dispos"},{"key":"1097_CR57","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.fct.2017.11.057","volume":"111","author":"T Isobe","year":"2018","unstructured":"Isobe T, Ohkawara S, Ochi S et al (2018) Naringenin glucuronidation in liver and intestine microsomes of humans, monkeys, rats, and mice. Food Chem Toxicol 111:417\u2013422. https:\/\/doi.org\/10.1016\/j.fct.2017.11.057","journal-title":"Food Chem Toxicol"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01097-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-01097-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01097-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:05:10Z","timestamp":1760522710000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-01097-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1097"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-01097-y","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]},"assertion":[{"value":"8 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","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":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"158"}}