{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T11:12:02Z","timestamp":1772622722240,"version":"3.50.1"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug-induced liver injury (DILI) is one of the most significant concerns in medical practice but yet it still cannot be fully recapitulated with existing in vivo, in vitro and in silico approaches. To address this challenge, Chen et\u00a0al. [ 1] developed a deep learning-based DILI prediction model based on chemical structure information alone. The reported model yielded an outstanding prediction performance (i.e. 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913 for AUC, accuracy, recall, precision, F1-score and specificity, respectively, on a test set), far outperforming all publicly available and similar in silico DILI models. This extraordinary model performance is counter-intuitive to what we know about the underlying biology of DILI and the principles and hypothesis behind this type of in silico approach. In this Letter to the Editor, we raise awareness of several issues concerning data curation, model validation and comparison practices, and data and model reproducibility.<\/jats:p>","DOI":"10.1093\/bib\/bbac237","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T14:15:30Z","timestamp":1658153730000},"source":"Crossref","is-referenced-by-count":5,"title":["Best practice and reproducible science are required to advance artificial intelligence in real-world applications"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5376-9003","authenticated-orcid":false,"given":"Zhichao","family":"Liu","sequence":"first","affiliation":[{"name":"National Center for Toxicological Research, US Food and Drug Administration , Jefferson, AR, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Li","sequence":"additional","affiliation":[{"name":"National Center for Toxicological Research, US Food and Drug Administration , Jefferson, AR, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3347-9180","authenticated-orcid":false,"given":"Skylar","family":"Connor","sequence":"additional","affiliation":[{"name":"National Center for Toxicological Research, US Food and Drug Administration , Jefferson, AR, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shraddha","family":"Thakkar","sequence":"additional","affiliation":[{"name":"Center for Drug Evaluation and Research, US FDA , Silver Spring, MD 20993, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruth","family":"Roberts","sequence":"additional","affiliation":[{"name":"ApconiX Ltd, Alderley Park , Alderley Edge, SK10 4TG, UK"},{"name":"University of Birmingham , Edgbaston, Birmingham, B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weida","family":"Tong","sequence":"additional","affiliation":[{"name":"National Center for Toxicological Research, US Food and Drug Administration , Jefferson, AR, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"2022071906091750500_ref1","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab503","article-title":"ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18","volume":"23","author":"Chen","year":"2022","journal-title":"Brief Bioinform"},{"key":"2022071906091750500_ref2","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1038\/s41572-019-0105-0","article-title":"Drug-induced liver injury","volume":"5","author":"Andrade","year":"2019","journal-title":"Nat Rev Dis Primers"},{"key":"2022071906091750500_ref3","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.jhep.2015.04.016","article-title":"Drug-induced liver injury: interactions between drug properties and host factors","volume":"63","author":"Chen","year":"2015","journal-title":"J Hepatol"},{"key":"2022071906091750500_ref4","doi-asserted-by":"crossref","first-page":"3486","DOI":"10.1002\/hep.31999","article-title":"Key characteristics of human Hepatotoxicants as a basis for identification and characterization of the causes of liver toxicity","volume":"74","author":"Rusyn","year":"2021","journal-title":"Hepatology (Baltimore, MD)"},{"key":"2022071906091750500_ref5","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1002\/hep.26175","article-title":"LiverTox: a website on drug-induced liver injury","volume":"57","author":"Hoofnagle","year":"2013","journal-title":"Hepatology"},{"key":"2022071906091750500_ref6","first-page":"H116","article-title":"Hepatox: database on hepatotoxic drugs","volume":"17","author":"Quinton","year":"1993","journal-title":"Gastroenterol Clin Biol"},{"key":"2022071906091750500_ref7","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.drudis.2016.02.015","article-title":"DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans","volume":"21","author":"Chen","year":"2016","journal-title":"Drug Discov Today"},{"key":"2022071906091750500_ref8","doi-asserted-by":"crossref","first-page":"3525","DOI":"10.1039\/D0CS00098A","article-title":"QSAR without borders","volume":"49","author":"Muratov","year":"2020","journal-title":"Chem Soc Rev"},{"key":"2022071906091750500_ref9","article-title":"Context in Artificial Intelligence: I. A Survey of the Literature","volume":"18","author":"Br\u00e9zillon","year":"1999","journal-title":"Computers and Artificial Intelligence"},{"key":"2022071906091750500_ref10","doi-asserted-by":"crossref","first-page":"0021","DOI":"10.1038\/s41562-016-0021","article-title":"A manifesto for reproducible science","volume":"1","author":"Munaf\u00f2","year":"2017","journal-title":"Nat Hum Behav"},{"key":"2022071906091750500_ref11","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1038\/533452a","article-title":"1,500 scientists lift the lid on reproducibility","volume":"533","author":"Baker","year":"2016","journal-title":"Nature"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/advance-article-pdf\/doi\/10.1093\/bib\/bbac237\/45017311\/bbac237.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/advance-article-pdf\/doi\/10.1093\/bib\/bbac237\/45017311\/bbac237.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T06:14:22Z","timestamp":1658211262000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac237\/6618241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,27]]},"references-count":11,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7,18]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac237","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,7,18]]},"published":{"date-parts":[[2022,6,27]]},"article-number":"bbac237"}}