{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T05:33:52Z","timestamp":1775108032997,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Food and Drug Safety of Korea","award":["20183MFDS410"],"award-info":[{"award-number":["20183MFDS410"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020M3A916A0036057"],"award-info":[{"award-number":["2020M3A916A0036057"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-00992-8","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T13:09:44Z","timestamp":1744117784000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model"],"prefix":"10.1186","volume":"17","author":[{"given":"Jiyeon","family":"Han","sequence":"first","affiliation":[]},{"given":"Wonho","family":"Zhung","sequence":"additional","affiliation":[]},{"given":"Insoo","family":"Jang","sequence":"additional","affiliation":[]},{"given":"Joongwon","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Min Ji","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Timothy Dain","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Seung Jun","family":"Kwack","sequence":"additional","affiliation":[]},{"given":"Kyu-Bong","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Daehee","family":"Hwang","sequence":"additional","affiliation":[]},{"given":"Byungwook","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Hyung Sik","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Woo Youn","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Sanghyuk","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"key":"992_CR1","unstructured":"David T (2021) Clinical development success rates and contributing factors 2011\u20132020"},{"issue":"12","key":"992_CR2","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1038\/nrd.2016.184","volume":"15","author":"RK Harrison","year":"2016","unstructured":"Harrison RK (2016) Phase II and phase III failures: 2013\u20132015. Nat Rev Drug Discov 15(12):817\u2013818","journal-title":"Nat Rev Drug Discov"},{"issue":"1","key":"992_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.nhtm.2014.08.001","volume":"2","author":"T Denayer","year":"2014","unstructured":"Denayer T, St\u00f6hr T, Van Roy M (2014) Animal models in translational medicine: validation and prediction. New Horizons Transl Med 2(1):5\u201311. https:\/\/doi.org\/10.1016\/j.nhtm.2014.08.001","journal-title":"New Horizons Transl Med"},{"issue":"1","key":"992_CR4","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.bcp.2013.08.006","volume":"87","author":"P McGonigle","year":"2014","unstructured":"McGonigle P, Ruggeri B (2014) Animal models of human disease: challenges in enabling translation. Biochem Pharmacol 87(1):162\u2013171. https:\/\/doi.org\/10.1016\/j.bcp.2013.08.006","journal-title":"Biochem Pharmacol"},{"issue":"1","key":"992_CR5","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.bcp.2013.06.020","volume":"87","author":"BA Ruggeri","year":"2014","unstructured":"Ruggeri BA, Camp F, Miknyoczki S (2014) Animal models of disease: pre-clinical animal models of cancer and their applications and utility in drug discovery. Biochem Pharmacol 87(1):150\u2013161. https:\/\/doi.org\/10.1016\/j.bcp.2013.06.020","journal-title":"Biochem Pharmacol"},{"issue":"1","key":"992_CR6","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1006\/rtph.2000.1399","volume":"32","author":"H Olson","year":"2000","unstructured":"Olson H, Betton G, Robinson D et al (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32(1):56\u201367. https:\/\/doi.org\/10.1006\/rtph.2000.1399","journal-title":"Regul Toxicol Pharmacol"},{"issue":"12","key":"992_CR7","doi-asserted-by":"publisher","first-page":"947","DOI":"10.7326\/0003-4819-137-12-200212170-00007","volume":"137","author":"G Ostapowicz","year":"2002","unstructured":"Ostapowicz G, Fontana RJ, Schi\u00f8dt FV et al (2002) Results of a prospective study of acute liver failure at 17 tertiary care centers in the United States. Ann Intern Med 137(12):947\u2013954. https:\/\/doi.org\/10.7326\/0003-4819-137-12-200212170-00007","journal-title":"Ann Intern Med"},{"issue":"15\u201316","key":"992_CR8","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.drudis.2011.05.007","volume":"16","author":"M Chen","year":"2011","unstructured":"Chen M, Vijay V, Shi Q et al (2011) FDA-approved drug labeling for the study of drug-induced liver injury. Drug Discov Today 16(15\u201316):697\u2013703. https:\/\/doi.org\/10.1016\/j.drudis.2011.05.007","journal-title":"Drug Discov Today"},{"issue":"4","key":"992_CR9","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.drudis.2016.02.015","volume":"21","author":"M Chen","year":"2016","unstructured":"Chen M, Suzuki A, Thakkar S et al (2016) DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans. Drug Discov Today 21(4):648\u2013653. https:\/\/doi.org\/10.1016\/j.drudis.2016.02.015","journal-title":"Drug Discov Today"},{"key":"992_CR10","unstructured":"LiverTox: Clinical and Research Information on Drug-Induced Liver Injury. 2022. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK547852\/. Accessed 17 Feb 2022"},{"key":"992_CR11","doi-asserted-by":"publisher","DOI":"10.23645\/epacomptox.6062623.v8","volume-title":"Invitrodb version 3.5 release","author":"M Feshuk","year":"2022","unstructured":"Feshuk M, Brown J, Davidson-Fritz S et al (2022) Invitrodb version 3.5 release. U.S. Environmental Protection Agency, Washington DC. https:\/\/doi.org\/10.23645\/epacomptox.6062623.v8"},{"issue":"1","key":"992_CR12","doi-asserted-by":"publisher","first-page":"D892","DOI":"10.1093\/nar\/gkm755","volume":"36","author":"M Waters","year":"2007","unstructured":"Waters M, Stasiewicz S, Alex Merrick B et al (2007) CEBS\u2014Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data. Nucleic Acids Res 36(1):D892\u2013D900. https:\/\/doi.org\/10.1093\/nar\/gkm755","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"992_CR13","doi-asserted-by":"publisher","first-page":"D928","DOI":"10.1093\/nar\/gku1004","volume":"43","author":"D Wishart","year":"2015","unstructured":"Wishart D, Arndt D, Pon A et al (2015) T3DB: the toxic exposome database. Nucleic Acids Res 43(D1):D928\u2013D934. https:\/\/doi.org\/10.1093\/nar\/gku1004","journal-title":"Nucleic Acids Res"},{"key":"992_CR14","unstructured":"Integrated risk information system, U.S. EPA. https:\/\/www.epa.gov\/iris. Accessed 3 Feb 2022"},{"key":"992_CR15","unstructured":"Agency for toxic substances and disease registry (ATSDR). https:\/\/www.atsdr.cdc.gov\/index.html. Accessed 3 Feb 2022"},{"key":"992_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-017-0247-6","volume":"9","author":"AJ Williams","year":"2017","unstructured":"Williams AJ, Grulke CM, Edwards J et al (2017) The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J Cheminform 9:1\u201327. https:\/\/doi.org\/10.1186\/s13321-017-0247-6","journal-title":"J Cheminform"},{"key":"992_CR17","unstructured":"NITE-CHRIP: NITE chemical risk information platform. https:\/\/www.nite.go.jp\/en\/chem\/chrip\/chrip_search\/systemTop. Accessed 20 Aug 2022"},{"key":"992_CR18","unstructured":"eChemPortal. https:\/\/www.echemportal.org\/echemportal. Accessed 20 Aug 2022"},{"issue":"7","key":"992_CR19","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1021\/tx1000865","volume":"23","author":"N Greene","year":"2010","unstructured":"Greene N, Fisk L, Naven RT et al (2010) Developing structure\u2014activity relationships for the prediction of hepatotoxicity. Chem Res Toxicol 23(7):1215\u20131222. https:\/\/doi.org\/10.1021\/tx1000865","journal-title":"Chem Res Toxicol"},{"key":"992_CR20","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s10822-016-9972-6","volume":"30","author":"H Zhang","year":"2016","unstructured":"Zhang H, Ding L, Zou Y et al (2016) Predicting drug-induced liver injury in human with Na\u00efve Bayes classifier approach. J Comput Aided Mol Des 30:889\u2013898. https:\/\/doi.org\/10.1007\/s10822-016-9972-6","journal-title":"J Comput Aided Mol Des"},{"issue":"12","key":"992_CR21","doi-asserted-by":"publisher","first-page":"2302","DOI":"10.1124\/dmd.110.035113","volume":"38","author":"S Ekins","year":"2010","unstructured":"Ekins S, Williams AJ, Xu JJ (2010) A predictive ligand-based Bayesian model for human drug-induced liver injury. Drug Metab Dispos 38(12):2302\u20132308. https:\/\/doi.org\/10.1124\/dmd.110.035113","journal-title":"Drug Metab Dispos"},{"issue":"5","key":"992_CR22","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1021\/acs.chemrestox.5b00465","volume":"29","author":"D Mulliner","year":"2016","unstructured":"Mulliner D, Schmidt F, Stolte M et al (2016) Computational models for human and animal hepatotoxicity with a global application scope. Chem Res Toxicol 29(5):757\u2013767. https:\/\/doi.org\/10.1021\/acs.chemrestox.5b00465","journal-title":"Chem Res Toxicol"},{"issue":"3\u20134","key":"992_CR23","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1002\/minf.201500055","volume":"35","author":"C Zhang","year":"2016","unstructured":"Zhang C, Cheng F, Li W et al (2016) In silico prediction of drug induced liver toxicity using substructure pattern recognition method. Mol Inf 35(3\u20134):136\u2013144. https:\/\/doi.org\/10.1002\/minf.201500055","journal-title":"Mol Inf"},{"key":"992_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13062-020-00285-0","volume":"16","author":"A Liu","year":"2021","unstructured":"Liu A, Walter M, Wright P et al (2021) Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure. Biol Direct 16:1\u201315. https:\/\/doi.org\/10.1186\/s13062-020-00285-0","journal-title":"Biol Direct"},{"issue":"1","key":"992_CR25","doi-asserted-by":"publisher","first-page":"17311","DOI":"10.1038\/s41598-017-17701-7","volume":"7","author":"H Hong","year":"2017","unstructured":"Hong H, Thakkar S, Chen M et al (2017) Development of decision forest models for prediction of drug-induced liver injury in humans using a large set of FDA-approved drugs. Sci Rep 7(1):17311. https:\/\/doi.org\/10.1038\/s41598-017-17701-7","journal-title":"Sci Rep"},{"issue":"1","key":"992_CR26","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1093\/toxsci\/kft189","volume":"136","author":"M Chen","year":"2013","unstructured":"Chen M, Hong H, Fang H et al (2013) Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs. Toxicol Sci 136(1):242\u2013249. https:\/\/doi.org\/10.1093\/toxsci\/kft189","journal-title":"Toxicol Sci"},{"key":"992_CR27","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/s12859-017-1638-4","volume":"18","author":"E Kim","year":"2017","unstructured":"Kim E, Nam H (2017) Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints. BMC Bioinform 18:25\u201334. https:\/\/doi.org\/10.1186\/s12859-017-1638-4","journal-title":"BMC Bioinform"},{"issue":"2","key":"992_CR28","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1093\/toxsci\/kfx099","volume":"158","author":"X-W Zhu","year":"2017","unstructured":"Zhu X-W, Li S-J (2017) In silico prediction of drug-induced liver injury based on adverse drug reaction reports. Toxicol Sci 158(2):391\u2013400. https:\/\/doi.org\/10.1093\/toxsci\/kfx099","journal-title":"Toxicol Sci"},{"issue":"1","key":"992_CR29","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1093\/toxsci\/kfy121","volume":"165","author":"H Ai","year":"2018","unstructured":"Ai H, Chen W, Zhang L et al (2018) Predicting drug-induced liver injury using ensemble learning methods and molecular fingerprints. Toxicol Sci 165(1):100\u2013107. https:\/\/doi.org\/10.1093\/toxsci\/kfy121","journal-title":"Toxicol Sci"},{"issue":"8","key":"992_CR30","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.3390\/ijms20081897","volume":"20","author":"S He","year":"2019","unstructured":"He S, Ye T, Wang R et al (2019) An in silico model for predicting drug-induced hepatotoxicity. Int J Mol Sci 20(8):1897. https:\/\/doi.org\/10.3390\/ijms20081897","journal-title":"Int J Mol Sci"},{"issue":"18","key":"992_CR31","doi-asserted-by":"publisher","first-page":"4426","DOI":"10.1093\/bioinformatics\/btac490","volume":"38","author":"HK Shin","year":"2022","unstructured":"Shin HK, Chun H-S, Lee S et al (2022) ToxSTAR: drug-induced liver injury prediction tool for the web environment. Bioinformatics 38(18):4426\u20134427. https:\/\/doi.org\/10.1093\/bioinformatics\/btac490","journal-title":"Bioinformatics"},{"issue":"2","key":"992_CR32","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1021\/acs.chemrestox.0c00374","volume":"34","author":"T Li","year":"2020","unstructured":"Li T, Tong W, Roberts R et al (2020) DeepDILI: deep learning-powered drug-induced liver injury prediction using model-level representation. Chem Res Toxicol 34(2):550\u2013565. https:\/\/doi.org\/10.1021\/acs.chemrestox.0c00374","journal-title":"Chem Res Toxicol"},{"issue":"24","key":"992_CR33","doi-asserted-by":"publisher","first-page":"7548","DOI":"10.3390\/molecules26247548","volume":"26","author":"M-G Kang","year":"2021","unstructured":"Kang M-G, Kang NS (2021) Predictive model for drug-induced liver injury using deep neural networks based on substructure space. Molecules 26(24):7548. https:\/\/doi.org\/10.3390\/molecules26247548","journal-title":"Molecules"},{"issue":"10","key":"992_CR34","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1021\/acs.jcim.5b00238","volume":"55","author":"Y Xu","year":"2015","unstructured":"Xu Y, Dai Z, Chen F et al (2015) Deep learning for drug-induced liver injury. J Chem Inf Model 55(10):2085\u20132093. https:\/\/doi.org\/10.1021\/acs.jcim.5b00238","journal-title":"J Chem Inf Model"},{"issue":"8","key":"992_CR35","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1093\/bioinformatics\/16.8.747","volume":"16","author":"A Lagunin","year":"2000","unstructured":"Lagunin A, Stepanchikova A, Filimonov D et al (2000) PASS: prediction of activity spectra for biologically active substances. Bioinformatics 16(8):747\u2013748. https:\/\/doi.org\/10.1093\/bioinformatics\/16.8.747","journal-title":"Bioinformatics"},{"key":"992_CR36","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3389\/fphar.2013.00038","volume":"4","author":"A Maunz","year":"2013","unstructured":"Maunz A, G\u00fctlein M, Rautenberg M et al (2013) Lazar: a modular predictive toxicology framework. Front Pharmacol 4:38. https:\/\/doi.org\/10.3389\/fphar.2013.00038","journal-title":"Front Pharmacol"},{"key":"992_CR37","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkae303","author":"P Banerjee","year":"2024","unstructured":"Banerjee P, Kemmler E, Dunkel M et al (2024) ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. https:\/\/doi.org\/10.1093\/nar\/gkae303","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"992_CR38","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1093\/bioinformatics\/bty707","volume":"35","author":"H Yang","year":"2019","unstructured":"Yang H, Lou C, Sun L et al (2019) admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 35(6):1067\u20131069. https:\/\/doi.org\/10.1093\/bioinformatics\/bty707","journal-title":"Bioinformatics"},{"issue":"14","key":"992_CR39","doi-asserted-by":"publisher","first-page":"2508","DOI":"10.1093\/bioinformatics\/bty135","volume":"34","author":"C Ji","year":"2018","unstructured":"Ji C, Svensson F, Zoufir A et al (2018) eMolTox: prediction of molecular toxicity with confidence. Bioinformatics 34(14):2508\u20132509. https:\/\/doi.org\/10.1093\/bioinformatics\/bty135","journal-title":"Bioinformatics"},{"issue":"D1","key":"992_CR40","doi-asserted-by":"publisher","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","volume":"47","author":"S Kim","year":"2019","unstructured":"Kim S, Chen J, Cheng T et al (2019) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47(D1):D1102\u2013D1109. https:\/\/doi.org\/10.1093\/nar\/gky1033","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"992_CR41","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart DS, Feunang YD, Guo AC et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46(D1):1074\u20131082. https:\/\/doi.org\/10.1093\/nar\/gkx1037","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"992_CR42","doi-asserted-by":"publisher","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","volume":"44","author":"M Kuhn","year":"2016","unstructured":"Kuhn M, Letunic I, Jensen LJ et al (2016) The SIDER database of drugs and side effects. Nucleic Acids Res 44(D1):D1075\u2013D1079. https:\/\/doi.org\/10.1093\/nar\/gkv1075","journal-title":"Nucleic Acids Res"},{"key":"992_CR43","unstructured":"Medical dictionary for regulatory activities (MedDRA). http:\/\/www.meddra.org\/. Accessed 5 June 2022"},{"key":"992_CR44","doi-asserted-by":"publisher","unstructured":"Brown TB (2020) Language models are few-shot learners. arXiv preprint. arXiv:2005.14165, https:\/\/doi.org\/10.48550\/arXiv.2005.14165","DOI":"10.48550\/arXiv.2005.14165"},{"issue":"6637","key":"992_CR45","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin Z, Akin H, Rao R et al (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637):1123\u20131130. https:\/\/doi.org\/10.1126\/science.ade2574","journal-title":"Science"},{"issue":"3","key":"992_CR46","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","volume":"4","author":"Y Wang","year":"2022","unstructured":"Wang Y, Wang J, Cao Z et al (2022) Molecular contrastive learning of representations via graph neural networks. Nat Mach Intell 4(3):279\u2013287. https:\/\/doi.org\/10.1038\/s42256-022-00447-x","journal-title":"Nat Mach Intell"},{"key":"992_CR47","doi-asserted-by":"publisher","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"992_CR48","doi-asserted-by":"publisher","unstructured":"Xu K, Hu W, Leskovec J, et al. (2018) How powerful are graph neural networks? arXiv preprint arXiv:1810.00826. https:\/\/doi.org\/10.48550\/arXiv.1810.00826","DOI":"10.48550\/arXiv.1810.00826"},{"issue":"1","key":"992_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-023-00796-8","volume":"16","author":"S Lee","year":"2024","unstructured":"Lee S, Yoo S (2024) InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism. J Cheminform 16(1):1. https:\/\/doi.org\/10.1186\/s13321-023-00796-8","journal-title":"J Cheminform"},{"key":"992_CR50","doi-asserted-by":"publisher","unstructured":"Sundararajan M, Taly A, Yan Q (2017) Axiomatic attribution for deep networks. Int Conf machine learning. https:\/\/doi.org\/10.48550\/arXiv.1703.01365","DOI":"10.48550\/arXiv.1703.01365"},{"key":"992_CR51","doi-asserted-by":"publisher","unstructured":"Simonyan K (2013) Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034. https:\/\/doi.org\/10.48550\/arXiv.1312.6034","DOI":"10.48550\/arXiv.1312.6034"},{"issue":"6","key":"992_CR52","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1021\/acs.chemrestox.7b00083","volume":"30","author":"H Yang","year":"2017","unstructured":"Yang H, Li J, Wu Z et al (2017) Evaluation of different methods for identification of structural alerts using chemical ames mutagenicity data set as a benchmark. Chem Res Toxicol 30(6):1355\u20131364. https:\/\/doi.org\/10.1021\/acs.chemrestox.7b00083","journal-title":"Chem Res Toxicol"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00992-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-00992-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00992-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T13:09:50Z","timestamp":1744117790000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-00992-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,8]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["992"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-00992-8","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,8]]},"assertion":[{"value":"3 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"48"}}