{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T10:56:49Z","timestamp":1790765809370,"version":"4.1.0"},"reference-count":16,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"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":["Computational Biology and Chemistry"],"published-print":{"date-parts":[[2027,2]]},"DOI":"10.1016\/j.compbiolchem.2026.109371","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T15:01:05Z","timestamp":1788188465000},"page":"109371","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Scaffold-shift uncertainty calibration in molecular activity prediction: A multi-target benchmark of coverage, efficiency, and risk-aware selection"],"prefix":"10.1016","volume":"126","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1814-9682","authenticated-orcid":false,"given":"Yanhui","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compbiolchem.2026.109371_bib1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.xphs.2020.09.055","article-title":"Predicting with confidence: using conformal prediction in drug discovery","volume":"110","author":"Alvarsson","year":"2021","journal-title":"J. Pharm. Sci."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib2","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1561\/2200000101","article-title":"A gentle introduction to conformal prediction and distribution-free uncertainty quantification","volume":"16","author":"Angelopoulos","year":"2023","journal-title":"Found. Trends Mach. Learn."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib3","doi-asserted-by":"crossref","first-page":"2887","DOI":"10.1021\/jm9602928","article-title":"The properties of known drugs. 1. Molecular frameworks","volume":"39","author":"Bemis","year":"1996","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib5","first-page":"1923","article-title":"Concepts and applications of conformal prediction in computational drug discovery","volume":"24","author":"Cortes-Ciriano","year":"2019","journal-title":"Drug. Discov. Today"},{"key":"10.1016\/j.compbiolchem.2026.109371_bib6","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib7","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"Lakshminarayanan","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib8","unstructured":"Landrum G., Tosco P., Kelley B., et al. RDKit: Open-source cheminformatics software. https:\/\/www.rdkit.org. Accessed 15 July 2026."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib9","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1021\/ci5001168","article-title":"Introducing conformal prediction in predictive modeling: a transparent and flexible alternative to applicability domain determination","volume":"54","author":"Norinder","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib10","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib11","first-page":"4791","article-title":"Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model","volume":"18","author":"Sahigara","year":"2013","journal-title":"Molecules"},{"key":"10.1016\/j.compbiolchem.2026.109371_bib12","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1021\/ci400084k","article-title":"Time-split cross-validation as a method for estimating the goodness of prospective prediction","volume":"53","author":"Sheridan","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib13","series-title":"Algorithmic Learning in a Random World","author":"Vovk","year":"2005"},{"key":"10.1016\/j.compbiolchem.2026.109371_bib14","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1021\/acs.jcim.7b00403","article-title":"Most ligand-based classification benchmarks reward memorization rather than generalization","volume":"58","author":"Wallach","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib15","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci."},{"key":"10.1016\/j.compbiolchem.2026.109371_bib16","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."}],"container-title":["Computational Biology and Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1476927126004986?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1476927126004986?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:56:14Z","timestamp":1790762174000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1476927126004986"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,2]]},"references-count":16,"alternative-id":["S1476927126004986"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109371","relation":{},"ISSN":["1476-9271"],"issn-type":[{"value":"1476-9271","type":"print"}],"subject":[],"published":{"date-parts":[[2027,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Scaffold-shift uncertainty calibration in molecular activity prediction: A multi-target benchmark of coverage, efficiency, and risk-aware selection","name":"articletitle","label":"Article Title"},{"value":"Computational Biology and Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109371","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"109371"}}