{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:49:57Z","timestamp":1792093797523,"version":"4.4.0"},"reference-count":67,"publisher":"Elsevier BV","issue":"6","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":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Drug Discovery Today"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.drudis.2026.104827","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T06:57:14Z","timestamp":1791269834000},"page":"104827","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["ADMET all-inclusive: Accountability for the full end-to-end model life cycle is key to success"],"prefix":"10.1016","volume":"31","author":[{"given":"Lara","family":"Kuhnke","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas H.","family":"G\u00f6ller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.drudis.2026.104827_b0005","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.addr.2012.02.008","article-title":"Advances in oral drug delivery: improved bioavailability of poorly absorbed drugs by tissue and cellular optimization","volume":"64","author":"Morishita","year":"2012","journal-title":"Adv Drug Deliv Rev"},{"key":"10.1016\/j.drudis.2026.104827_b0010","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.2174\/156802605774297038","article-title":"Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection","volume":"5","author":"Balani","year":"2005","journal-title":"Curr Top Med Chem"},{"key":"10.1016\/j.drudis.2026.104827_b0015","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1146\/annurev-pharmtox-040323-040828","article-title":"Artificial intelligence for drug discovery: are we there yet?","volume":"64","author":"Hasselgren","year":"2024","journal-title":"Annu Rev Pharmacol Toxicol"},{"key":"10.1016\/j.drudis.2026.104827_b0020","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1038\/nrd1470","article-title":"Can the pharmaceutical industry reduce attrition rates?","volume":"3","author":"Kola","year":"2004","journal-title":"Nat Rev Drug Discov"},{"key":"10.1016\/j.drudis.2026.104827_b0025","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/S1359-6446(97)01099-4","article-title":"Managing the drug discovery\/development interface","volume":"2","author":"Kennedy","year":"1997","journal-title":"Drug Discov Today"},{"key":"10.1016\/j.drudis.2026.104827_b0030","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1021\/ci500666m","article-title":"eCounterscreening: using QSAR predictions to prioritize testing for off-target activities and setting the balance between benefit and risk","volume":"55","author":"Sheridan","year":"2015","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejmech.2025.117947","article-title":"Leveraging machine learning predicted confidence for boosting assay submission and decision-making efficiencies","volume":"297","author":"Bassani","year":"2025","journal-title":"Eur J Med Chem"},{"key":"10.1016\/j.drudis.2026.104827_b0040","unstructured":"DrugPatentWatch website. Advancements in in silico ADMET modeling: a 20-year journey of machine learning in drug discovery at Bayer Pharma. Accessed 16 December 2025."},{"key":"10.1016\/j.drudis.2026.104827_b0045","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.1016\/j.drudis.2020.07.001","article-title":"Bayer\u2019s in silico ADMET platform: a journey of machine learning over the past two decades","volume":"25","author":"G\u00f6ller","year":"2020","journal-title":"Drug Discov Today"},{"key":"10.1016\/j.drudis.2026.104827_b0050","first-page":"61","article-title":"Machine learning applied to the modeling of pharmacological and ADMET endpoints","volume":"vol. 2390","author":"G\u00f6ller","year":"2022"},{"key":"10.1016\/j.drudis.2026.104827_b0055","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.drudis.2011.10.023","article-title":"Toward in silico structure-based ADMET prediction in drug discovery","volume":"17","author":"Moroy","year":"2012","journal-title":"Drug Discov Today"},{"key":"10.1016\/j.drudis.2026.104827_b0060","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1002\/cmdc.200600155","article-title":"Prediction of ADMET properties","volume":"1","author":"Norinder","year":"2006","journal-title":"ChemMedChem"},{"key":"10.1016\/j.drudis.2026.104827_b0065","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1016\/j.drudis.2019.03.015","article-title":"ADMET modeling approaches in drug discovery","volume":"24","author":"Ferreira","year":"2019","journal-title":"Drug Discov Today"},{"key":"10.1016\/j.drudis.2026.104827_b0070","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.3390\/pharmaceutics15112619","article-title":"The trends and future prospective of in silico models from the viewpoint of ADME evaluation in drug discovery","volume":"15","author":"Komura","year":"2023","journal-title":"Pharmaceutics"},{"key":"10.1016\/j.drudis.2026.104827_b0075","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.5599\/admet.2772","article-title":"Leveraging machine learning models in evaluating ADMET properties for drug discovery and development","volume":"13","author":"Venkataraman","year":"2026","journal-title":"ADMET DMPK"},{"key":"10.1016\/j.drudis.2026.104827_b0080","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/bs.apha.2025.01.004","article-title":"ADMET tools in the digital era: applications and limitations","volume":"103","author":"Shinde","year":"2025","journal-title":"Adv Pharmacol"},{"key":"10.1016\/j.drudis.2026.104827_b0085","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1038\/nrd1032","article-title":"ADMET in silico modelling: towards prediction paradise?","volume":"2","author":"Van de Waterbeemd","year":"2003","journal-title":"Nat Rev Drug Discov"},{"key":"10.1016\/j.drudis.2026.104827_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2024.103945","article-title":"Augmenting DMTA using predictive AI modelling at AstraZeneca","volume":"29","author":"Ghiandoni","year":"2024","journal-title":"Drug Discov Today"},{"key":"10.1016\/j.drudis.2026.104827_b0095","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1038\/s41587-025-02555-6","article-title":"Fresh from the biotech pipeline: FDA approvals settle in 2024, but what next?","volume":"43","author":"Senior","year":"2025","journal-title":"Nat Biotechnol"},{"key":"10.1016\/j.drudis.2026.104827_b0100","doi-asserted-by":"crossref","first-page":"5764","DOI":"10.1038\/s41467-024-49979-3","article-title":"Application of machine learning models for property prediction to targeted protein degraders","volume":"15","author":"Peteani","year":"2024","journal-title":"Nat Commun"},{"key":"10.1016\/j.drudis.2026.104827_b0105","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.1021\/acs.jcim.2c01317","article-title":"Serverless prediction of peptide properties with recurrent neural networks","volume":"63","author":"Ansari","year":"2023","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0110","doi-asserted-by":"crossref","first-page":"11814","DOI":"10.1021\/acs.jmedchem.4c00417","article-title":"Machine-learning-guided peptide drug discovery: development of GLP-1 receptor agonists with improved drug properties","volume":"67","author":"Nielsen","year":"2024","journal-title":"J Med Chem"},{"key":"10.1016\/j.drudis.2026.104827_b0115","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.molstruc.2011.05.011","article-title":"Quantitative structure-activity relationship study of antioxidative peptide by using different sets of amino acid descriptors","volume":"998","author":"Li","year":"2011","journal-title":"J Mol Struct"},{"key":"10.1016\/j.drudis.2026.104827_b0120","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/1758-2946-3-28","article-title":"AZOrange-High performance open source machine learning for QSAR modeling in a graphical programming environment","volume":"3","author":"St\u00e5lring","year":"2011","journal-title":"J Cheminform"},{"key":"10.1016\/j.drudis.2026.104827_b0125","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s10822-013-9648-4","article-title":"QSAR workbench: automating QSAR modeling to drive compound design","volume":"27","author":"Cox","year":"2013","journal-title":"J Comput Aided Mol Des"},{"key":"10.1016\/j.drudis.2026.104827_b0130","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.1021\/acs.jcim.4c02122","article-title":"Employing automated machine learning (AutoML) methods to facilitate the in silico ADMET properties prediction","volume":"65","author":"Han","year":"2025","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0135","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1002\/minf.201000061","article-title":"Best practices for QSAR model development, validation, and exploitation","volume":"29","author":"Tropsha","year":"2010","journal-title":"Mol Inform"},{"key":"10.1016\/j.drudis.2026.104827_b0140","doi-asserted-by":"crossref","first-page":"44","DOI":"10.3390\/molecules25010044","article-title":"Modeling physico-chemical ADMET endpoints with multitask graph convolutional networks","volume":"25","author":"Montanari","year":"2019","journal-title":"Molecules"},{"key":"10.1016\/j.drudis.2026.104827_b0145","doi-asserted-by":"crossref","DOI":"10.1002\/minf.202100113","article-title":"ADMET predictability at Boehringer Ingelheim: state-of-the-art, and do bigger datasets or algorithms make a difference?","volume":"41","author":"Aleksic","year":"2022","journal-title":"Mol Inform"},{"key":"10.1016\/j.drudis.2026.104827_b0150","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s13321-017-0230-2","article-title":"Efficiency of different measures for defining the applicability domain of classification models","volume":"9","author":"Klingspohn","year":"2017","journal-title":"J Cheminform"},{"key":"10.1016\/j.drudis.2026.104827_b0155","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1021\/acsmedchemlett.4c00290","article-title":"Machine learning ADME models in practice: four guidelines from a successful lead optimization case study","volume":"15","author":"Rich","year":"2024","journal-title":"ACS Med Chem Lett"},{"key":"10.1016\/j.drudis.2026.104827_b0160","unstructured":"Optibrium. Glowing molecule in stardrop. Accessed 1 April 2026. https:\/\/optibrium.com\/products\/stardrop\/features\/glowing-molecule-in-stardrop\/."},{"key":"10.1016\/j.drudis.2026.104827_b0165","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1007\/s00894-022-05373-8","article-title":"ADMETboost: a web server for accurate ADMET prediction","volume":"28","author":"Tian","year":"2022","journal-title":"J Mol Model"},{"key":"10.1016\/j.drudis.2026.104827_b0170","doi-asserted-by":"crossref","first-page":"776","DOI":"10.3390\/molecules28020776","article-title":"Evaluation of free online ADMET tools for academic or small biotech environments","volume":"28","author":"Dulsat","year":"2023","journal-title":"Molecules"},{"key":"10.1016\/j.drudis.2026.104827_b0175","doi-asserted-by":"crossref","first-page":"W5","DOI":"10.1093\/nar\/gkab255","article-title":"ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties","volume":"49","author":"Xiong","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"10.1016\/j.drudis.2026.104827_b0180","doi-asserted-by":"crossref","first-page":"W432","DOI":"10.1093\/nar\/gkae298","article-title":"admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties","volume":"52","author":"Gu","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"10.1016\/j.drudis.2026.104827_b0185","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1021\/ci700267w","article-title":"Advanced biological and chemical discovery (ABCD): a centralizing discovery knowledge in an inherently decentralized world","volume":"47","author":"Agrafiotis","year":"2007","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0190","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1021\/ci500598e","article-title":"FOCUS \u2013 development of a global communication and modeling platform for applied and computational medicinal chemists","volume":"55","author":"Stiefl","year":"2015","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0195","unstructured":"D360 Platform. Certara website. D360 Platform brochure. Accessed March 2026."},{"key":"10.1016\/j.drudis.2026.104827_b0200","unstructured":"LiveDesign Schr\u00f6dinger website. LiveDesign platform. Accessed 28 March 2026."},{"key":"10.1016\/j.drudis.2026.104827_b0205","unstructured":"Design Hub. ChemAxon website. Design Hub. Accessed 28 March 2026."},{"key":"10.1016\/j.drudis.2026.104827_b0210","unstructured":"Pipeline Pilot, version 2023. Dassault Syst\u00e8mes BIOVIA Corp., San Diego, CA; 2022."},{"key":"10.1016\/j.drudis.2026.104827_b0215","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1021\/jm00147a001","article-title":"Computer-assisted drug design","volume":"28","author":"Hopfinger","year":"1985","journal-title":"J Med Chem"},{"key":"10.1016\/j.drudis.2026.104827_b0220","doi-asserted-by":"crossref","first-page":"4893","DOI":"10.1021\/acs.jcim.9b00460","article-title":"Prediction of oral bioavailability in rats: transferring insights from in vitro correlations to (deep) machine learning models using in silico model outputs and chemical structure parameters","volume":"59","author":"Schneckener","year":"2019","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0225","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1002\/cmdc.201700097","article-title":"Site of metabolism prediction based on ab initio derived atom representations","volume":"12","author":"Finkelmann","year":"2017","journal-title":"ChemMedChem"},{"key":"10.1016\/j.drudis.2026.104827_b0230","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"},{"key":"10.1016\/j.drudis.2026.104827_b0235","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1021\/acs.jcim.3c00799","article-title":"MELLODDY: cross-pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information","volume":"64","author":"Heyndrickx","year":"2024","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0240","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s10822-023-00547-9","article-title":"A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat","volume":"38","author":"F\u00fchrer","year":"2024","journal-title":"J Comput Aided Mol Des"},{"key":"10.1016\/j.drudis.2026.104827_b0245","article-title":"Prediction of human pharmacokinetics from chemical structure: combining mechanistic modeling with machine learning","author":"Gruber","year":"2023","journal-title":"J Pharm Sci"},{"key":"10.1016\/j.drudis.2026.104827_b0250","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2024.1415266","article-title":"Quantum chemical calculations of nitrosamine activation and deactivation pathways for carcinogenicity risk assessment","volume":"15","author":"G\u00f6ller","year":"2024","journal-title":"Front Pharmacol"},{"key":"10.1016\/j.drudis.2026.104827_b0255","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1021\/acs.chemrestox.4c00347","article-title":"Data exploration for target predictions using proprietary and publicly available data sets","volume":"38","author":"Smajic","year":"2025","journal-title":"Chem Res Toxicol"},{"key":"10.1016\/j.drudis.2026.104827_b0260","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.drudis.2011.04.005","article-title":"Rendezvous in chemical space? Comparing the small molecule compound libraries of Bayer and Schering","volume":"16","author":"Schamberger","year":"2011","journal-title":"Drug Discov Today"},{"key":"10.1016\/j.drudis.2026.104827_b0265","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1016\/j.drudis.2012.10.011","article-title":"Big Pharma screening collections: more of the same or unique libraries? The Astrazeneca-Bayer Pharma AG case","volume":"18","author":"Kogej","year":"2013","journal-title":"Drug Discov Today"},{"key":"10.1016\/j.drudis.2026.104827_b0270","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1021\/ci500585w","article-title":"Best of both worlds: combining pharma data and state of the art modeling technology to improve in silico pKa prediction","volume":"55","author":"Fraczkiewicz","year":"2015","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0275","doi-asserted-by":"crossref","DOI":"10.1002\/minf.202400088","article-title":"Best of both worlds: an expansion of the state of the art pKa model with data from three industrial partners","volume":"43","author":"Fraczkiewicz","year":"2024","journal-title":"Mol Inform"},{"key":"10.1016\/j.drudis.2026.104827_b0280","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1038\/s42256-025-00991-2","article-title":"Data-driven federated learning in drug discovery with knowledge distillation","volume":"7","author":"Hanser","year":"2025","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.drudis.2026.104827_b0285","unstructured":"Schr\u00f6dinger. Schr\u00f6dinger software platform."},{"key":"10.1016\/j.drudis.2026.104827_b0290","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1021\/ci0503210","article-title":"In silico prediction of buffer solubility based on quantum-mechanical and HQSAR- and topology-based descriptors","volume":"46","author":"G\u00f6ller","year":"2006","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0295","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1002\/cmdc.200800384","article-title":"CypScore: quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory","volume":"4","author":"Hennemann","year":"2009","journal-title":"ChemMedChem"},{"key":"10.1016\/j.drudis.2026.104827_b0300","doi-asserted-by":"crossref","DOI":"10.1002\/minf.201800115","article-title":"Gaussian process regression models for the prediction of hydrogen bond acceptor strengths","volume":"38","author":"Bauer","year":"2019","journal-title":"Mol Inform"},{"key":"10.1016\/j.drudis.2026.104827_b0305","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13321-019-0381-4","article-title":"Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies","volume":"11","author":"Bauer","year":"2019","journal-title":"J Cheminform"},{"key":"10.1016\/j.drudis.2026.104827_b0310","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1021\/acs.jcim.8b00758","article-title":"Mechanistic reactivity descriptors for the prediction of Ames mutagenicity of primary aromatic amines","volume":"59","author":"Kuhnke","year":"2019","journal-title":"J Chem Inf Model"},{"key":"10.1016\/j.drudis.2026.104827_b0320","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1039\/D5DD00398A","article-title":"When machine learning models learn chemistry I: quantifying explainability with matched molecular pairs","volume":"5","author":"Janssen","year":"2026","journal-title":"Digit Discov"},{"key":"10.1016\/j.drudis.2026.104827_b0325","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1039\/D5DD00399G","article-title":"When machine learning models learn chemistry II: applying WISP to real-world examples","volume":"5","author":"Janssen","year":"2026","journal-title":"Digit Discov"},{"key":"10.1016\/j.drudis.2026.104827_b0315","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1021\/acs.chemrestox.2c00217","article-title":"Predicting DNA-reactivity of N-nitrosamines: a quantum chemical approach","volume":"35","author":"Wenzel","year":"2022","journal-title":"Chem Res Toxicol"},{"key":"10.1016\/j.drudis.2026.104827_b0330","article-title":"Accelerating drug discovery through agentic AI: a multi-agent approach to laboratory automation in the DMTA cycle","author":"Fehlis","year":"2025","journal-title":"arXiv"},{"key":"10.1016\/j.drudis.2026.104827_b0335","doi-asserted-by":"crossref","DOI":"10.1016\/j.matt.2025.102263","article-title":"El Agente: an autonomous agent for quantum chemistry","volume":"8","author":"Zou","year":"2025","journal-title":"Matter"}],"container-title":["Drug Discovery Today"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1359644626002321?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1359644626002321?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:29:06Z","timestamp":1792092546000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1359644626002321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":67,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["S1359644626002321"],"URL":"https:\/\/doi.org\/10.1016\/j.drudis.2026.104827","relation":{},"ISSN":["1359-6446"],"issn-type":[{"value":"1359-6446","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ADMET all-inclusive: Accountability for the full end-to-end model life cycle is key to success","name":"articletitle","label":"Article Title"},{"value":"Drug Discovery Today","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.drudis.2026.104827","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104827"}}