{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T21:42:08Z","timestamp":1780522928574,"version":"3.54.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:00:00Z","timestamp":1771027200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Austrian Federal Ministry of Labour and Economy"},{"DOI":"10.13039\/501100006012","name":"Christian Doppler Forschungsgesellschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006012","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Boehringer-Ingelheim RCV GmbH"},{"name":"BASF SE"},{"DOI":"10.13039\/501100003065","name":"University of Vienna","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003065","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Predicting likely sites of metabolism (SOMs), i.e., the atoms in a molecule where metabolic reactions are initiated, is an important component of the computational development pipeline for pharmaceuticals, agrochemicals, and cosmetics. Among SOM prediction tools, FAME3, introduced in 2019, is one of only a few non-commercial models capable of predicting both Phase\u00a01 and Phase\u00a02 SOMs for a wide range of xenobiotics. However, its original implementation posed challenges in maintainability, scalability, and interoperability, which hindered broader adoption. To overcome these limitations, we developed FAME3R, an enhanced version of FAME3 designed to improve computational efficiency and facilitate integration with contemporary cheminformatics workflows. FAME3R introduces several new features, including a novel reliability assessment method based on Shannon entropy and the option to select among various featurization strategies. The tool is available as an open-source Python package, offering both a Python API and a CLI for flexible usage. Additionally, trained FAME3R models can be accessed via a GUI and a REST API hosted on the NERDD web platform.<\/jats:p>","DOI":"10.1186\/s13321-026-01161-1","type":"journal-article","created":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T12:46:18Z","timestamp":1771073178000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["FAME3R: an efficient, practical and reliable open-source tool for predicting phase\u00a01 and phase\u00a02 sites of metabolism"],"prefix":"10.1186","volume":"18","author":[{"given":"Roxane Axel","family":"Jacob","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo","family":"Gaskin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Seidel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelica","family":"Mazzolari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Kirchmair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,14]]},"reference":[{"key":"1161_CR1","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.drudis.2012.01.017","volume":"17","author":"B Testa","year":"2012","unstructured":"Testa B, Pedretti A, Vistoli G (2012) Reactions and enzymes in the metabolism of drugs and other xenobiotics. Drug Discov Today 17:549\u2013560","journal-title":"Drug Discov Today"},{"key":"1161_CR2","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, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G (2015) Predicting drug metabolism: experiment and\/or computation? Nat Rev Drug Discov 14:387\u2013404","journal-title":"Nat Rev Drug Discov"},{"key":"1161_CR3","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1111\/cbdd.13445","volume":"93","author":"JD Tyzack","year":"2019","unstructured":"Tyzack JD, Kirchmair J (2019) Computational methods and tools to predict cytochrome P450 metabolism for drug discovery. Chem Biol Drug Des 93:377\u2013386","journal-title":"Chem Biol Drug Des"},{"key":"1161_CR4","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.1080\/17460441.2020.1798926","volume":"15","author":"S Kar","year":"2020","unstructured":"Kar S, Leszczynski J (2020) Open access in silico tools to predict the ADMET profiling of drug candidates. Expert Opin Drug Discov 15:1473\u20131487","journal-title":"Expert Opin Drug Discov"},{"key":"1161_CR5","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.dmpk.2019.11.006","volume":"35","author":"H Kato","year":"2020","unstructured":"Kato H (2020) Computational prediction of cytochrome P450 inhibition and induction. DMPK 35:30\u201344","journal-title":"DMPK"},{"key":"1161_CR6","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1080\/03602532.2020.1765793","volume":"52","author":"LE Russell","year":"2020","unstructured":"Russell LE et al (2020) Advances in the study of drug metabolism - symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX). Drug Metab Rev 52:395\u2013407","journal-title":"Drug Metab Rev"},{"key":"1161_CR7","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1080\/03602532.2021.1923728","volume":"53","author":"NV Dhuria","year":"2021","unstructured":"Dhuria NV, Haro B, Kapadia A, Lobo KA, Matusow B, Schleiff MA, Tantoy C, Sodhi JK (2021) Recent developments in predicting CYP-independent metabolism. Drug Metab Rev 53:188\u2013206","journal-title":"Drug Metab Rev"},{"key":"1161_CR8","doi-asserted-by":"publisher","first-page":"1260","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 15:1260","journal-title":"Pharmaceutics"},{"key":"1161_CR9","doi-asserted-by":"publisher","first-page":"6970","DOI":"10.1021\/jm050529c","volume":"48","author":"G Cruciani","year":"2005","unstructured":"Cruciani G, Carosati E, Boeck BD, Ethirajulu K, Mackie C, Howe T, Vianello R (2005) MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J Med Chem 48:6970\u20136979","journal-title":"J Med Chem"},{"key":"1161_CR10","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.1093\/bioinformatics\/btv087","volume":"31","author":"A Rudik","year":"2015","unstructured":"Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V (2015) SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds. Bioinformatics 31:2046\u20132048","journal-title":"Bioinformatics"},{"key":"1161_CR11","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, de Bruyn Kops C, Pedretti A, Testa B, Vistoli G, Svozil D, Kirchmair J (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","journal-title":"J Chem Inf Model"},{"key":"1161_CR12","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1021\/acs.jcim.9b00836","volume":"60","author":"NL Dang","year":"2020","unstructured":"Dang NL, Matlock MK, Hughes TB, Swamidass SJ (2020) The metabolic rainbow: deep learning phase I metabolism in five colors. J Chem Inf Model 60:1146\u20131164","journal-title":"J Chem Inf Model"},{"key":"1161_CR13","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad089","volume":"39","author":"V Porokhin","year":"2023","unstructured":"Porokhin V, Liu L-P, Hassoun S (2023) Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products. Bioinformatics 39:btad089","journal-title":"Bioinformatics"},{"key":"1161_CR14","doi-asserted-by":"publisher","first-page":"8462","DOI":"10.1021\/acs.jcim.5c00762","volume":"65","author":"RA Jacob","year":"2025","unstructured":"Jacob RA, Wieder O, Chen Y, Mazzolari A, Bergner A, Schleifer K-J, Kirchmair J (2025) Site-of-metabolism prediction with aleatoric and epistemic uncertainty quantification. J Chem Inf Model 65:8462\u20138474","journal-title":"J Chem Inf Model"},{"key":"1161_CR15","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","journal-title":"J Med Chem"},{"key":"1161_CR16","unstructured":"Hirte\u00a0S NERDD: Next-generation E-Resource for Drug Discovery. https:\/\/nerdd.univie.ac.at Accessed: 2025-10-25"},{"key":"1161_CR17","doi-asserted-by":"publisher","DOI":"10.26434\/chemrxiv-2025-thhkd","author":"S Hirte","year":"2025","unstructured":"Hirte S, Scholz V-A, Kirchmair J (2025) A scalable microservices platform for deploying machine learning models in drug discovery and beyond. ChemRxiv. https:\/\/doi.org\/10.26434\/chemrxiv-2025-thhkd","journal-title":"ChemRxiv"},{"key":"1161_CR18","unstructured":"Seidel\u00a0T Chemical data processing toolkit source code repository. https:\/\/github.com\/molinfo-vienna\/CDPKit Accessed: 2025-10-25"},{"issue":"1","key":"1161_CR19","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13321-017-0220-4","volume":"9","author":"EL Willighagen","year":"2017","unstructured":"Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chert\u00f3 M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C (2017) The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 9(1):33. https:\/\/doi.org\/10.1186\/s13321-017-0220-4","journal-title":"J Cheminform"},{"key":"1161_CR20","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1002\/jcc.540100804","volume":"10","author":"M Clark","year":"1989","unstructured":"Clark M, Cramer RD, Opdenbosch NV (1989) Validation of the general purpose tripos 52 force field. J Comput Chem 10:982\u20131012","journal-title":"J Comput Chem"},{"key":"1161_CR21","doi-asserted-by":"crossref","unstructured":"Jacob\u00a0RA, Gaskin\u00a0L FAME 3R: A Fast, Compact, Flexible, and Practical Re-Design of the FAME 3 Model for Predicting Sites-of-Metabolism. https:\/\/github.com\/molinfo-vienna\/FAME3R Accessed: 2025-10-25","DOI":"10.26434\/chemrxiv-2025-1vnp0"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-026-01161-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-026-01161-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-026-01161-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T16:51:26Z","timestamp":1774284686000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13321-026-01161-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,14]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1161"],"URL":"https:\/\/doi.org\/10.1186\/s13321-026-01161-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2025-1vnp0","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,14]]},"assertion":[{"value":"29 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"AM is an owner of the MetaQSAR database. RJ, LG, TS, YC, and JK declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"37"}}