{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T16:35:37Z","timestamp":1777480537862,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"ALTERNATIVE","award":["101037090"],"award-info":[{"award-number":["101037090"]}]},{"name":"ALTERNATIVE","award":["101037090"],"award-info":[{"award-number":["101037090"]}]},{"name":"Explainable AI for Molecules - AiChemist","award":["101120466"],"award-info":[{"award-number":["101120466"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:sec>\n            <jats:title>Abstract<\/jats:title>\n            <jats:p>In recent years, the integration of Artificial Intelligence and Machine Learning methods with biochemical and biomedical research has revolutionized the field of toxicology, significantly advancing our understanding of the toxicological effects of chemicals on biological systems. Cardiovascular diseases remain the leading global cause of death. The constant exposure to multiple chemicals with potential cardiotoxic effects, including environmental contaminants, pesticides, food additives, and drugs, can significantly contribute to these adverse health outcomes. Traditional methods for assessing chemical hazards and their impact on biological function heavily rely on experimental assays and animal studies, which are often time-consuming, resource-intensive, and limited in scalability. To overcome these limitations in silico methods have emerged as indispensable tools in toxicological research, reducing the need for traditional in vivo testing and conserving valuable resources in terms of time and cost. In this study, Artificial Intelligence methods are used as first-tier components within an Integrated Approach to Testing and Assessment. We explored the potential benefits of using Multitask Neural Networks, where multiple levels of cardiotoxicity information are combined to enhance model performance. Multitask learning, based on specific architectures such as Mixture of Experts (MoE), showed promising results and surpasses the performance of single-task baseline models. When predicting a holdout set, multitask model achieved high performance on twelve different endpoints related to cardiotoxicity defined by Adverse Outcome Pathways Network. The best developed model achieved a balanced accuracy of 78%, a sensitivity of 80%, and a specificity of 76% across all endpoints in the holdout set.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Scientific contribution<\/jats:title>\n            <jats:p>An advanced multitask model was developed to predict cardiotoxicity mechanisms induced by small molecules. The model demonstrates broad mechanistic coverage and achieves performance comparable to, or exceeding, state-of-the-art methods. These results suggest that the model could serve as a valuable first-tier component in advanced New Approach Methodologies for prioritizing chemicals for further testing.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s13321-025-01072-7","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T09:38:22Z","timestamp":1756460302000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Mixture of experts for multitask learning in cardiotoxicity assessment"],"prefix":"10.1186","volume":"17","author":[{"given":"Edoardo Luca","family":"Vigan\u00f2","sequence":"first","affiliation":[]},{"given":"Mateusz","family":"Iwan","sequence":"additional","affiliation":[]},{"given":"Erika","family":"Colombo","sequence":"additional","affiliation":[]},{"given":"Davide","family":"Ballabio","sequence":"additional","affiliation":[]},{"given":"Alessandra","family":"Roncaglioni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"issue":"156","key":"1072_CR1","doi-asserted-by":"publisher","first-page":"106734","DOI":"10.1016\/j.envint.2021.106734","volume":"1","author":"FM P\u00e9rez-Carrascosa","year":"2021","unstructured":"P\u00e9rez-Carrascosa FM, G\u00f3mez-Pe\u00f1a C, Echeverr\u00eda R, Mole\u00f3n JJ, Melchor JM, Garc\u00eda-Ruiz A, Navarro-Espigares JL, Cabeza-Barrera J, Martin-Olmedo P, Ortigosa-Garc\u00eda JC, Arrebola JP (2021) Historical exposure to persistent organic pollutants and cardiovascular disease: a 15-year longitudinal analysis focused on pharmaceutical consumption in primary care. Environ Int 1(156):106734. https:\/\/doi.org\/10.1016\/j.envint.2021.106734","journal-title":"Environ Int"},{"key":"1072_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.taap.2018.06.004","volume":"353","author":"N Georgiadis","year":"2018","unstructured":"Georgiadis N, Tsarouhas K, Tsitsimpikou C, Vardavas A, Rezaee R, Germanakis I, Tsatsakis A, Stagos D, Kouretas D (2018) Pesticides and cardiotoxicity. Where do we stand? Toxicol Appl Pharmacol 353:1\u201314","journal-title":"Toxicol Appl Pharmacol"},{"key":"1072_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph13020229","author":"MM Sekhotha","year":"2016","unstructured":"Sekhotha MM, Monyeki KD, Sibuyi ME (2016) Exposure to agrochemicals and cardiovascular disease: a review. Int J Environ Res Public Health. https:\/\/doi.org\/10.3390\/ijerph13020229","journal-title":"Int J Environ Res Public Health"},{"key":"1072_CR4","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1002\/med.21476","volume":"38","author":"P Mlad\u011bnka","year":"2018","unstructured":"Mlad\u011bnka P, Applov\u00e1 L, Pato\u010dka J, Costa VM, Remiao F, Pourov\u00e1 J, Mlad\u011bnka A, Karl\u00ed\u010dkov\u00e1 J, Jahod\u00e1\u0159 L, Vopr\u0161alov\u00e1 M et al (2018) Comprehensive review of cardiovascular toxicity of drugs and related agents. Med Res Rev 38:1332\u20131403","journal-title":"Med Res Rev"},{"key":"1072_CR5","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1021\/acs.chemrestox.0c00382","volume":"34","author":"S Krishna","year":"2021","unstructured":"Krishna S, Berridge B, Kleinstreuer N (2021) High-throughput screening to identify chemical cardiotoxic potential. Chem Res Toxicol 34:566\u2013583. https:\/\/doi.org\/10.1021\/acs.chemrestox.0c00382","journal-title":"Chem Res Toxicol"},{"key":"1072_CR6","doi-asserted-by":"publisher","first-page":"337","DOI":"10.14573\/ALTEX.2301121","volume":"40","author":"A Schaffert","year":"2023","unstructured":"Schaffert A, Murugadoss S, Mertens B, Paparella M (2023) Cardiotoxicity of chemicals: current regulatory guidelines, knowledge gaps, and needs. ALTEX-Altern Anim Exp 40:337\u2013340. https:\/\/doi.org\/10.14573\/ALTEX.2301121","journal-title":"ALTEX-Altern Anim Exp"},{"key":"1072_CR7","doi-asserted-by":"publisher","DOI":"10.1515\/REVEH-2024-0072","author":"G Donzelli","year":"2024","unstructured":"Donzelli G, Gehring R, Murugadoss S, Roos T, Schaffert A, Linzalone N (2024) A critical review on the toxicological and epidemiological evidence integration for assessing human health risks to environmental chemical exposures. Rev Environ Health. https:\/\/doi.org\/10.1515\/REVEH-2024-0072","journal-title":"Rev Environ Health"},{"key":"1072_CR8","doi-asserted-by":"publisher","first-page":"248","DOI":"10.14573\/ALTEX.2304111","volume":"41","author":"T Roos","year":"2024","unstructured":"Roos T, Leenaars C, Schaffert A, Paparella M, Murugadoss S, Mertens B, Linzalone N, Donzelli G, Ritskes-Hoitinga M, Gehring R (2024) Pollutant exposure and myocardial injury: protocol and progress report for a toxicological systematic mapping review. Altex 41:248\u2013259. https:\/\/doi.org\/10.14573\/ALTEX.2304111","journal-title":"Altex"},{"key":"1072_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/FTOX.2022.964553","author":"AO Stucki","year":"2022","unstructured":"Stucki AO, Barton-Maclaren TS, Bhuller Y, Henriquez JE, Henry TR, Hirn C, Miller-Holt J, Nagy EG, Perron MM, Ratzlaff DE et al (2022) Use of new approach methodologies (NAMs) to meet regulatory requirements for the assessment of industrial chemicals and pesticides for effects on human health. Front Toxicol. https:\/\/doi.org\/10.3389\/FTOX.2022.964553","journal-title":"Front Toxicol"},{"key":"1072_CR10","doi-asserted-by":"crossref","unstructured":"Saavedra LM, Martinez JC, Duchowicz PR (2024) Advances of the QSAR approach as an alternative strategy in the environmental risk assessment. In: QSAR in safety evaluation and risk assessment. Academic Press, pp 117\u2013137","DOI":"10.1016\/B978-0-443-15339-6.00032-1"},{"key":"1072_CR11","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/JM4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977\u20135010. https:\/\/doi.org\/10.1021\/JM4004285","journal-title":"J Med Chem"},{"key":"1072_CR12","volume-title":"Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment","author":"K Roy","year":"2015","unstructured":"Roy K, Kar S, Das RN (2015) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, London"},{"key":"1072_CR13","doi-asserted-by":"publisher","unstructured":"OECD (2014) Guidance document on the validation of (quantitative) structure\u2013activity relationship [(Q)SAR] Models. OECD. https:\/\/doi.org\/10.1787\/9789264085442-EN","DOI":"10.1787\/9789264085442-EN"},{"key":"1072_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENVRES.2024.118429","author":"G Donzelli","year":"2024","unstructured":"Donzelli G, Sera F, Morales MA, Vozzi F, Roos T, Schaffert A, Paparella M, Murugadoss S, Mertens B, Gehring R et al (2024) A systematic review and meta-analysis of human population studies on the association between exposure to toxic environmental chemicals and left ventricular dysfunction (LVD). Environ Res. https:\/\/doi.org\/10.1016\/J.ENVRES.2024.118429","journal-title":"Environ Res"},{"key":"1072_CR15","unstructured":"Sivakumar M, AOP Wiki (n.d.) Adverse outcome pathway wiki. Mitochondrial complexes inhibition leading to heart failure via increased myocardial oxidative stress."},{"key":"1072_CR16","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1002\/etc.34","volume":"29","author":"GT Ankley","year":"2010","unstructured":"Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung MW, Johnson RD, Mount DR, Nichols JW, Russom CL, Schmieder PK et al (2010) Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29:730\u2013741","journal-title":"Environ Toxicol Chem"},{"issue":"11","key":"1072_CR17","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.3390\/MOLECULES25112615","volume":"25","author":"KE Choi","year":"2020","unstructured":"Choi KE, Balupuri A, Kang NS (2020) The study on the HERG blocker prediction using chemical fingerprint analysis. Molecules 25(11):2615. https:\/\/doi.org\/10.3390\/MOLECULES25112615","journal-title":"Molecules"},{"key":"1072_CR18","doi-asserted-by":"publisher","DOI":"10.1186\/S12859-019-2814-5","author":"HM Lee","year":"2019","unstructured":"Lee HM, Yu MS, Kazmi SR, Oh SY, Rhee KH, Bae MA, Lee BH, Shin DS, Oh KS, Ceong H et al (2019) Computational determination of HERG-related cardiotoxicity of drug candidates. BMC Bioinformatics. https:\/\/doi.org\/10.1186\/S12859-019-2814-5","journal-title":"BMC Bioinformatics"},{"key":"1072_CR19","doi-asserted-by":"publisher","first-page":"87","DOI":"10.4161\/CHAN.2.2.6004","volume":"2","author":"BT Priest","year":"2008","unstructured":"Priest BT, Bell IM, Garcia ML (2008) Role of HERG potassium channel assays in drug development. Channels 2:87\u201393. https:\/\/doi.org\/10.4161\/CHAN.2.2.6004","journal-title":"Channels"},{"key":"1072_CR20","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa075","author":"JY Ryu","year":"2020","unstructured":"Ryu JY, Lee MY, Lee JH, Lee BH, Oh K-S (2020) DeepHIT: A deep learning framework for prediction of HERG-induced cardiotoxicity. Bioinformatics. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa075","journal-title":"Bioinformatics"},{"key":"1072_CR21","doi-asserted-by":"publisher","DOI":"10.3389\/FPHAR.2022.951083","volume":"13","author":"P Delre","year":"2022","unstructured":"Delre P, Lavado GJ, Lamanna G, Saviano M, Roncaglioni A, Benfenati E, Mangiatordi GF, Gadaleta D (2022) Ligand-based prediction of HERG-mediated cardiotoxicity based on the integration of different machine learning techniques. Front Pharmacol 13:951083. https:\/\/doi.org\/10.3389\/FPHAR.2022.951083","journal-title":"Front Pharmacol"},{"key":"1072_CR22","doi-asserted-by":"publisher","first-page":"225","DOI":"10.2174\/1568016033477432","volume":"1","author":"AB Zolotoy","year":"2003","unstructured":"Zolotoy AB, Plouvier BP, Beatch GB, Hayes ES, Wall RA, Walker MJA (2003) Physicochemical determinants for drug induced blockade of HERG potassium channels: effect of charge and charge shielding. Curr Med Chem Cardiovasc Hematol Agents 1:225\u2013241. https:\/\/doi.org\/10.2174\/1568016033477432","journal-title":"Curr Med Chem Cardiovasc Hematol Agents"},{"key":"1072_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-021-00541-z","author":"A Karim","year":"2021","unstructured":"Karim A, Lee M, Balle T, Sattar A (2021) Cardiotox net: a robust predictor for HERG channel blockade based on deep learning meta-feature ensembles. J Cheminform. https:\/\/doi.org\/10.1186\/s13321-021-00541-z","journal-title":"J Cheminform"},{"key":"1072_CR24","doi-asserted-by":"publisher","first-page":"6007","DOI":"10.1021\/acs.jcim.0c00884","volume":"60","author":"VB Siramshetty","year":"2020","unstructured":"Siramshetty VB, Nguyen DT, Martinez NJ, Southall NT, Simeonov A, Zakharov AV (2020) Critical assessment of artificial intelligence methods for prediction of HERG channel inhibition in the \u201cbig data\u201d era. J Chem Inf Model 60:6007\u20136019. https:\/\/doi.org\/10.1021\/acs.jcim.0c00884","journal-title":"J Chem Inf Model"},{"key":"1072_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/J.CHEMOSPHERE.2020.127068","volume":"256","author":"E Goya-Jorge","year":"2020","unstructured":"Goya-Jorge E, Giner RM, Sylla-Iyarreta Veit\u00eda M, Gozalbes R, Barigye SJ (2020) Predictive modeling of aryl hydrocarbon receptor (AhR) agonism. Chemosphere 256:127068. https:\/\/doi.org\/10.1016\/J.CHEMOSPHERE.2020.127068","journal-title":"Chemosphere"},{"key":"1072_CR26","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1080\/00365510260390019","volume":"62","author":"D Y\u00fccel","year":"2002","unstructured":"Y\u00fccel D, Aydo\u01e7du S, \u015eene\u015f M, Topkaya B\u00c7, Nebio\u01e7lu S (2002) Evidence of increased oxidative stress by simple measurements in patients with dilated cardiomyopathy. Scand J Clin Lab Invest 62:463\u2013468. https:\/\/doi.org\/10.1080\/00365510260390019","journal-title":"Scand J Clin Lab Invest"},{"key":"1072_CR27","doi-asserted-by":"publisher","unstructured":"Fan CD, Sun JY, Fu XT, Hou YJ, Li Y, Yang MF, Fu XY, Sun BL (2017) Astaxanthin attenuates homocysteine-induced cardiotoxicity in vitro and in vivo by inhibiting mitochondrial dysfunction and oxidative damage. Front Physiol 8:1041. https:\/\/doi.org\/10.3389\/FPHYS.2017.01041\/PDF","DOI":"10.3389\/FPHYS.2017.01041\/PDF"},{"key":"1072_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/J.FCT.2020.111494","author":"J Cotterill","year":"2020","unstructured":"Cotterill J, Price N, Rorije E, Peijnenburg A (2020) Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools. Food Chem Toxicol. https:\/\/doi.org\/10.1016\/J.FCT.2020.111494","journal-title":"Food Chem Toxicol"},{"key":"1072_CR29","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1021\/acs.jcim.8b00297","volume":"58","author":"D Gadaleta","year":"2018","unstructured":"Gadaleta D, Manganelli S, Roncaglioni A, Toma C, Benfenati E, Mombelli E (2018) Qsar modeling of ToxCast assays relevant to the molecular initiating events of AOPs leading to hepatic steatosis. J Chem Inf Model 58:1501\u20131517. https:\/\/doi.org\/10.1021\/acs.jcim.8b00297","journal-title":"J Chem Inf Model"},{"key":"1072_CR30","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3390\/toxics12010087","volume":"12","author":"EL Vigan\u00f2","year":"2024","unstructured":"Vigan\u00f2 EL, Ballabio D, Roncaglioni A (2024) Artificial intelligence and machine learning methods to evaluate cardiotoxicity following the adverse outcome pathway frameworks. Toxics 12:87. https:\/\/doi.org\/10.3390\/toxics12010087","journal-title":"Toxics"},{"key":"1072_CR31","doi-asserted-by":"publisher","unstructured":"Garcia de Lomana M, Marin Zapata PA, Montanari F (2023) Predicting the mitochondrial toxicity of small molecules: insights from mechanistic assays and cell painting data. Chem Res Toxicol 36(7):1107\u201320. https:\/\/doi.org\/10.1021\/acs.chemrestox.3c00086.","DOI":"10.1021\/acs.chemrestox.3c00086"},{"key":"1072_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109480","author":"S Boonsom","year":"2025","unstructured":"Boonsom S, Chamnansil P, Boonseng S, Srisongkram T (2025) ToxSTK: a multi-target toxicity assessment utilizing molecular structure and stacking ensemble learning. Comput Biol Med. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.109480","journal-title":"Comput Biol Med"},{"issue":"1","key":"1072_CR33","doi-asserted-by":"publisher","first-page":"4908","DOI":"10.1038\/s41598-023-31169-8","volume":"13","author":"B Sharma","year":"2023","unstructured":"Sharma B, Chenthamarakshan V, Dhurandhar A, Pereira S, Hendler JA, Dordick JS, Das P (2023) Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Sci Rep 13(1):4908. https:\/\/doi.org\/10.1038\/s41598-023-31169-8","journal-title":"Sci Rep"},{"key":"1072_CR34","doi-asserted-by":"publisher","DOI":"10.3390\/molecules27185827","author":"V Consonni","year":"2022","unstructured":"Consonni V, Gosetti F, Termopoli V, Todeschini R, Valsecchi C, Ballabio D (2022) Multi-task neural networks and molecular fingerprints to enhance compound identification from LC-MS\/MS data. Molecules. https:\/\/doi.org\/10.3390\/molecules27185827","journal-title":"Molecules"},{"issue":"5","key":"1072_CR35","doi-asserted-by":"publisher","first-page":"2200257","DOI":"10.1002\/minf.202200257","volume":"42","author":"Y Yuan Li","year":"2023","unstructured":"Yuan Li Y, Chen L, Pu C, Zang C, Yan Y, Chen Y, Zhang Y, Liu H (2023) Co-Model for chemical toxicity prediction based on multi-task deep learning. Mol Inform. 42(5):2200257","journal-title":"Mol Inform."},{"key":"1072_CR36","doi-asserted-by":"publisher","first-page":"W612","DOI":"10.1093\/NAR\/GKV352","volume":"43","author":"M Davies","year":"2015","unstructured":"Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, Bellis L, Overington JP (2015) ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res 43:W612. https:\/\/doi.org\/10.1093\/NAR\/GKV352","journal-title":"Nucleic Acids Res"},{"key":"1072_CR37","unstructured":"Integrated Chemical Environment (ICE). Available online: https:\/\/ice.ntp.niehs.nih.gov\/. Accessed on 4 February 2025"},{"issue":"11","key":"1072_CR38","doi-asserted-by":"publisher","first-page":"103770","DOI":"10.1101\/2023.07.06.548029","volume":"28","author":"Y Qu","year":"2023","unstructured":"Qu Y, Li T, Liu Z, Li D, Tong W (2023) DICTrank: the largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling. Drug Discov Today 28(11):103770. https:\/\/doi.org\/10.1101\/2023.07.06.548029","journal-title":"Drug Discov Today"},{"issue":"6","key":"1072_CR39","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1039\/C8SC04175J","volume":"10","author":"R Winter","year":"2019","unstructured":"Winter R, Montanari F, No\u00e9 F, Clevert DA (2019) Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations. Chem Sci 10(6):1692\u20131701","journal-title":"Chem Sci"},{"key":"1072_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0315-6","author":"D Gadaleta","year":"2018","unstructured":"Gadaleta D, Lombardo A, Toma C, Benfenati E (2018) A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications. J Cheminform. https:\/\/doi.org\/10.1186\/s13321-018-0315-6","journal-title":"J Cheminform"},{"key":"1072_CR41","unstructured":"Ryan N (2017) A user\u2019s guide for accessing and interpreting ToxCastTM data"},{"key":"1072_CR42","doi-asserted-by":"crossref","unstructured":"Consonni V, Todeschini R (2009) Molecular descriptors. In: Recent advances in QSAR studies: methods and applications, pp 29\u2013102. Springer Netherlands, Dordrecht","DOI":"10.1007\/978-1-4020-9783-6_3"},{"key":"1072_CR43","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0258-y","author":"H Moriwaki","year":"2018","unstructured":"Moriwaki H, Tian YS, Kawashita N, Takagi T (2018) Mordred: A molecular descriptor calculator. J Cheminform. https:\/\/doi.org\/10.1186\/s13321-018-0258-y","journal-title":"J Cheminform"},{"key":"1072_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/S13321-024-00830-3","volume":"16","author":"D Boldini","year":"2024","unstructured":"Boldini D, Ballabio D, Consonni V, Todeschini R, Grisoni F, Sieber SA (2024) Effectiveness of molecular fingerprints for exploring the chemical space of natural products. J Cheminform 16:1\u201316. https:\/\/doi.org\/10.1186\/S13321-024-00830-3","journal-title":"J Cheminform"},{"key":"1072_CR45","unstructured":"Chithrananda S, Grand G, Ramsundar B (2020) ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885"},{"key":"1072_CR46","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/CI100050T\/ASSET\/IMAGES\/MEDIUM\/CI-2010-00050T_0018.GIF","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742\u2013754. https:\/\/doi.org\/10.1021\/CI100050T\/ASSET\/IMAGES\/MEDIUM\/CI-2010-00050T_0018.GIF","journal-title":"J Chem Inf Model"},{"key":"1072_CR47","doi-asserted-by":"crossref","unstructured":"Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7482\u20137491","DOI":"10.1109\/CVPR.2018.00781"},{"key":"1072_CR48","doi-asserted-by":"publisher","unstructured":"Gadaleta D, Mangiatordi GF, Catto M, Carotti A, Nicolotti O. Applicability domain for QSAR models: where theory meets reality. Int J Quant struct\u2013Prop Relationsh (IJQSPR). 2016;1(1):45\u201363. https:\/\/doi.org\/10.4018\/IJQSPR.2016010102.","DOI":"10.4018\/IJQSPR.2016010102"},{"issue":"5","key":"1072_CR49","doi-asserted-by":"publisher","first-page":"4791","DOI":"10.3390\/MOLECULES17054791","volume":"17","author":"F Sahigara","year":"2012","unstructured":"Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17(5):4791\u20134810. https:\/\/doi.org\/10.3390\/MOLECULES17054791","journal-title":"Molecules"},{"key":"1072_CR50","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp 93\u2013104.","DOI":"10.1145\/342009.335388"},{"key":"1072_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2025.117575","author":"N Gambacorta","year":"2025","unstructured":"Gambacorta N, Mastrolorito F, Togo MV, Amenduni V, Mele M, Liantonio A, Mele A, De Luca A, Altomare CD, Belgiovine V et al (2025) CUPID: a free drug discovery platform for the explainable multi-ion channel assessment of cardiotoxicity. Eur J Med Chem. https:\/\/doi.org\/10.1016\/j.ejmech.2025.117575","journal-title":"Eur J Med Chem"},{"key":"1072_CR52","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/S13321-021-00541-Z","volume":"13","author":"A Karim","year":"2021","unstructured":"Karim A, Lee M, Balle T, Sattar A (2021) Cardiotox net: a robust predictor for HERG channel blockade based on deep learning meta-feature ensembles. J Cheminform 13:60. https:\/\/doi.org\/10.1186\/S13321-021-00541-Z","journal-title":"J Cheminform"},{"key":"1072_CR53","doi-asserted-by":"publisher","first-page":"H1453","DOI":"10.1152\/AJPHEART.00554.2015","volume":"309","author":"ZV Varga","year":"2015","unstructured":"Varga ZV, Ferdinandy P, Liaudet L, Pacher P (2015) Drug-induced mitochondrial dysfunction and cardiotoxicity. Am J Physiol Heart Circ Physiol 309:H1453\u2013H1467. https:\/\/doi.org\/10.1152\/AJPHEART.00554.2015","journal-title":"Am J Physiol Heart Circ Physiol"},{"key":"1072_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMTOX.2021.100189","volume":"20","author":"F Bringezu","year":"2021","unstructured":"Bringezu F, Carlos G\u00f3mez-Tamayo J, Pastor M (2021) Ensemble prediction of mitochondrial toxicity using machine learning technology. Comput Toxicol 20:100189. https:\/\/doi.org\/10.1016\/J.COMTOX.2021.100189","journal-title":"Comput Toxicol"},{"key":"1072_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2021.128067","author":"W Tang","year":"2021","unstructured":"Tang W, Liu W, Wang Z, Hong H, Chen J (2021) Machine learning models on chemical inhibitors of mitochondrial electron transport chain. J Hazard Mater. https:\/\/doi.org\/10.1016\/j.jhazmat.2021.128067","journal-title":"J Hazard Mater"},{"issue":"5","key":"1072_CR56","doi-asserted-by":"publisher","first-page":"2000005","DOI":"10.1002\/minf.202000005","volume":"39","author":"J Hemmerich","year":"2020","unstructured":"Hemmerich J, Troger F, F\u00fczi B (2020) Using machine learning methods and structural alerts for prediction of mitochondrial toxicity. Mol Inf 39(5):2000005","journal-title":"Mol Inf"},{"issue":"1","key":"1072_CR57","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1038\/s42003-022-03763-5","volume":"5","author":"S Seal","year":"2022","unstructured":"Seal S, Carreras-Puigvert J, Trapotsi MA, Yang H, Spjuth O, Bender A (2022) Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection. Commun Biol 5(1):858. https:\/\/doi.org\/10.1038\/s42003-022-03763-5","journal-title":"Commun Biol"},{"key":"1072_CR58","doi-asserted-by":"publisher","unstructured":"Vigan\u00f2 EL, Colombo E, Ballabio D, Roncaglioni A (2025) Artificial intelligence methods for\u00a0evaluating mitochondrial dysfunction: exploring various chemical notations suitable for\u00a0neural language processing models. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 14894 LNCS, pp 116\u2013131. https:\/\/doi.org\/10.1007\/978-3-031-72381-0_10.","DOI":"10.1007\/978-3-031-72381-0_10"},{"key":"1072_CR59","unstructured":"Online Chemical Modeling Environment. Available at: https:\/\/www.ochem.eu\/home\/show.do. Accessed on 28 May 2025."}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01072-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-01072-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01072-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T05:19:26Z","timestamp":1757481566000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-01072-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1072"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-01072-7","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,29]]},"assertion":[{"value":"10 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 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":"135"}}