{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T04:41:19Z","timestamp":1792644079892,"version":"4.5.0"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Medicinal Chemistry"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.ejmech.2026.119312","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T15:01:33Z","timestamp":1788793293000},"page":"119312","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["XenoSoM: A deep learning framework for site of metabolism prediction of xenobiotics"],"prefix":"10.1016","volume":"320","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2743-1827","authenticated-orcid":false,"given":"Alessio","family":"Macorano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serena","family":"Vittorio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4625-6736","authenticated-orcid":false,"given":"Filippo","family":"Lunghini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Pedretti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulio","family":"Vistoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea Rosario","family":"Beccari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejmech.2026.119312_bib1","doi-asserted-by":"crossref","first-page":"2220","DOI":"10.2174\/138161209788682460","article-title":"Metabolite identification and profiling in drug design: current practice and future directions","volume":"15","author":"Zhang","year":"2009","journal-title":"Curr. Pharm. Des."},{"key":"10.1016\/j.ejmech.2026.119312_bib2","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1038\/nrd4581","article-title":"Predicting drug metabolism: experiment and\/or computation?","volume":"14","author":"Kirchmair","year":"2015","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejmech.2026.119312_bib3","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1021\/ci200542m","article-title":"Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms","volume":"52","author":"Kirchmair","year":"2012","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib4","doi-asserted-by":"crossref","DOI":"10.3390\/ijms262412087","article-title":"MetaQM: exploring the role of QM calculations in drug metabolism prediction","volume":"26","author":"Macorano","year":"2025","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejmech.2026.119312_bib5","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1186\/s12859-018-2194-2","article-title":"In silico prediction of potential chemical reactions mediated by human enzymes","volume":"19","author":"Yu","year":"2018","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2026.119312_bib6","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1021\/acsmedchemlett.8b00603","article-title":"Prediction of UGT-mediated metabolism using the manually curated MetaQSAR database","volume":"10","author":"Mazzolari","year":"2019","journal-title":"ACS Med. Chem. Lett."},{"key":"10.1016\/j.ejmech.2026.119312_bib7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s13321-022-00626-3","article-title":"In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network","volume":"14","author":"Huang","year":"2022","journal-title":"J. Cheminform."},{"key":"10.1016\/j.ejmech.2026.119312_bib8","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1021\/acs.chemrestox.5b00017","article-title":"Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione","volume":"28","author":"Hughes","year":"2015","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejmech.2026.119312_bib9","doi-asserted-by":"crossref","first-page":"3400","DOI":"10.1021\/acs.jcim.9b00376","article-title":"Fame 3: predicting the sites of metabolism in synthetic compounds and natural products for phase 1 and phase 2 metabolic enzymes","volume":"59","author":"\u0160\u00edcho","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib10","doi-asserted-by":"crossref","first-page":"4702","DOI":"10.1021\/acs.jcim.0c00360","article-title":"Metabolic Forest: predicting the diverse structures of drug metabolites","volume":"60","author":"Hughes","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib11","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1021\/acs.chemrestox.0c00224","article-title":"GLORYx: prediction of the Metabolites resulting from phase 1 and phase 2 Biotransformations of Xenobiotics","volume":"34","author":"De Bruyn Kops","year":"2021","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejmech.2026.119312_bib12","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1021\/ci400518g","article-title":"XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks","volume":"53","author":"Zaretzki","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib13","doi-asserted-by":"crossref","first-page":"3174","DOI":"10.1093\/bioinformatics\/btz037","article-title":"SMARTCyp 3.0: enhanced cytochrome P450 site-of-metabolism prediction server","volume":"35","author":"Olsen","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2026.119312_bib14","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1021\/ml100016x","article-title":"SMARTCyp: a 2D method for prediction of cytochrome P450-Mediated drug metabolism","volume":"1","author":"Rydberg","year":"2010","journal-title":"ACS Med. Chem. Lett."},{"key":"10.1016\/j.ejmech.2026.119312_bib15","doi-asserted-by":"crossref","DOI":"10.3390\/ijms241311064","article-title":"MetaSpot: a general approach for recognizing the reactive atoms undergoing metabolic reactions based on the MetaQSAR database","volume":"24","author":"Mazzolari","year":"2023","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejmech.2026.119312_bib16","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1021\/acs.jmedchem.7b01473","article-title":"MetaQSAR: an integrated database engine to manage and analyze metabolic data","volume":"61","author":"Pedretti","year":"2018","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejmech.2026.119312_bib17","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/s42256-021-00438-4","article-title":"Geometry-enhanced molecular representation learning for property prediction","volume":"4","author":"Fang","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.ejmech.2026.119312_bib18","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13321-020-00479-8","article-title":"Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models","volume":"13","author":"Jiang","year":"2021","journal-title":"J. Cheminform."},{"key":"10.1016\/j.ejmech.2026.119312_bib19","author":"Fabian"},{"key":"10.1016\/j.ejmech.2026.119312_bib20","author":"Ying"},{"key":"10.1016\/j.ejmech.2026.119312_bib21","author":"Chithrananda"},{"key":"10.1016\/j.ejmech.2026.119312_bib22","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad089","article-title":"Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products","volume":"39","author":"Porokhin","year":"2023","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2026.119312_bib23","doi-asserted-by":"crossref","first-page":"8462","DOI":"10.1021\/acs.jcim.5c00762","article-title":"Site-of-Metabolism prediction with aleatoric and epistemic uncertainty quantification","volume":"65","author":"Jacob","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib24","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.1021\/ci400503s","article-title":"FAst MEtabolizer (FAME): a rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes","volume":"53","author":"Kirchmair","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib25","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1021\/acs.jcim.7b00250","article-title":"Fame 2: simple and effective machine learning model of cytochrome P450 regioselectivity","volume":"57","author":"\u0160\u00edcho","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib26","doi-asserted-by":"crossref","first-page":"6970","DOI":"10.1021\/jm050529c","article-title":"MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist","volume":"48","author":"Cruciani","year":"2005","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejmech.2026.119312_bib27","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1016\/j.csbj.2024.05.024","article-title":"Addressing docking pose selection with structure-based deep learning: recent advances, challenges and opportunities","volume":"23","author":"Vittorio","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.ejmech.2026.119312_bib28","doi-asserted-by":"crossref","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","article-title":"The ChEMBL database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods","volume":"52","author":"Zdrazil","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2026.119312_bib29","series-title":"BIO Online Structure Activity Relationship Database) GVK Biosciences Private Limited, Plot No. 28 A, IDA Nacharam, Hyderabad, India","year":"2010"},{"key":"10.1016\/j.ejmech.2026.119312_bib30","unstructured":"CDDI Database (https:\/\/www.cortellis.com\/drugdiscovery\/), (n.d.)."},{"key":"10.1016\/j.ejmech.2026.119312_bib31","series-title":"Predicting Drug Metabolism: Concepts and Challenges*","author":"Testa","year":"2004"},{"key":"10.1016\/j.ejmech.2026.119312_bib32","unstructured":"RDKit: open-source cheminformatics. https:\/\/www.rdkit.org, (n.d.)."},{"key":"10.1016\/j.ejmech.2026.119312_bib33","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1021\/acsmeasuresciau.6c00019","article-title":"MetabolEx: platform for small-molecule metabolites generation and 2 metabolic pathway prediction","volume":"6","author":"Lunghini","year":"2026","journal-title":"ACS Meas. Sci. Au"},{"key":"10.1016\/j.ejmech.2026.119312_bib34","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1021\/c160017a018","article-title":"The generation of a unique machine description for chemical Structures-A technique developed at chemical abstracts service","volume":"5","author":"Morgan","year":"1965","journal-title":"J. Chem. Doc."},{"key":"10.1016\/j.ejmech.2026.119312_bib35","doi-asserted-by":"crossref","DOI":"10.1038\/s43246-022-00315-6","article-title":"Graph neural networks for materials science and chemistry","volume":"3","author":"Reiser","year":"2022","journal-title":"Commun. Mater."},{"key":"10.1016\/j.ejmech.2026.119312_bib36","author":"Fey"},{"key":"10.1016\/j.ejmech.2026.119312_bib37","author":"Kipf"},{"key":"10.1016\/j.ejmech.2026.119312_bib38","author":"Xu"},{"key":"10.1016\/j.ejmech.2026.119312_bib39","unstructured":"P. Veli\u010dkoviveli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. L\u00ec, Y. Bengio, GRAPH ATTENTION NETWORKS, n.d."},{"key":"10.1016\/j.ejmech.2026.119312_bib40","author":"Hu"},{"key":"10.1016\/j.ejmech.2026.119312_bib41","author":"Brody"},{"key":"10.1016\/j.ejmech.2026.119312_bib42","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism","volume":"63","author":"Xiong","year":"2020","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejmech.2026.119312_bib43","author":"Akiba"},{"key":"10.1016\/j.ejmech.2026.119312_bib44","author":"Kingma"},{"key":"10.1016\/j.ejmech.2026.119312_bib45","author":"Ying"},{"key":"10.1016\/j.ejmech.2026.119312_bib46","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s13321-026-01161-1","article-title":"FAME3R: an efficient, practical and reliable open-source tool for predicting phase 1 and phase 2 sites of metabolism","volume":"18","author":"Jacob","year":"2026","journal-title":"J. Cheminform."},{"key":"10.1016\/j.ejmech.2026.119312_bib47","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.yrtph.2014.11.005","article-title":"Comparison of cramer classification between toxtree, the OECD QSAR toolbox and expert judgment","volume":"71","author":"Bhatia","year":"2015","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"10.1016\/j.ejmech.2026.119312_bib48","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1021\/ci900313u","article-title":"Chemical domain of QSAR models from atom-centered fragments","volume":"49","author":"K\u00fchne","year":"2009","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib49","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.3390\/ijms17101686","article-title":"Construction of metabolism prediction models for CYP450 3A4, 2D6, and 2C9 based on microsomal metabolic reaction system","volume":"17","author":"He","year":"2016","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejmech.2026.119312_bib50","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.2040","article-title":"D-CyPre: a machine learning-based tool for accurate prediction of human CYP450 enzyme metabolic sites","volume":"10","author":"Yang","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.ejmech.2026.119312_bib51","doi-asserted-by":"crossref","first-page":"14066","DOI":"10.1021\/acs.jmedchem.2c01303","article-title":"Predicting regioselectivity of AO, CYP, FMO, and UGT metabolism using quantum mechanical simulations and machine learning","volume":"65","author":"\u00d6eren","year":"2022","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejmech.2026.119312_bib52","doi-asserted-by":"crossref","first-page":"3340","DOI":"10.1021\/acs.jcim.3c00275","article-title":"Predicting regioselectivity of cytosolic sulfotransferase metabolism for drugs","volume":"63","author":"\u00d6eren","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2026.119312_bib53","doi-asserted-by":"crossref","first-page":"234","DOI":"10.3390\/molecules30020234","article-title":"Atom-driven and knowledge-based hydrolysis metabolite assessment for environmental organic chemicals","volume":"30","author":"Liu","year":"2025","journal-title":"Molecules"},{"key":"10.1016\/j.ejmech.2026.119312_bib54","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1002\/cmdc.201800309","article-title":"MetScore: site of metabolism prediction beyond cytochrome P450 enzymes","volume":"13","author":"Finkelmann","year":"2018","journal-title":"ChemMedChem"}],"container-title":["European Journal of Medicinal Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0223523426007579?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0223523426007579?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T04:26:17Z","timestamp":1792643177000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0223523426007579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":54,"alternative-id":["S0223523426007579"],"URL":"https:\/\/doi.org\/10.1016\/j.ejmech.2026.119312","relation":{},"ISSN":["0223-5234"],"issn-type":[{"value":"0223-5234","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"XenoSoM: A deep learning framework for site of metabolism prediction of xenobiotics","name":"articletitle","label":"Article Title"},{"value":"European Journal of Medicinal Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejmech.2026.119312","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Masson SAS.","name":"copyright","label":"Copyright"}],"article-number":"119312"}}