{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T22:29:41Z","timestamp":1786487381890,"version":"build-2736575974"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Arch Toxicol"],"DOI":"10.1007\/s00204-026-04519-4","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T21:54:11Z","timestamp":1786485251000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cardiosim-Tox: an interpretable multitask deep learning QSAR platform with multimodal feature fusion for predicting hERG, Cav1.2, and Nav1.5 blockade risk and potency"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5330-2008","authenticated-orcid":false,"given":"Fauzan Syarif","family":"Nursyafi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5397-6451","authenticated-orcid":false,"given":"Byunggyu","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3517-5372","authenticated-orcid":false,"given":"Junhyeok","family":"Eom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0842-027X","authenticated-orcid":false,"given":"Rahmafatin Nurul","family":"Izza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0629-6597","authenticated-orcid":false,"given":"Abdul Latif","family":"Fauzan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9274-1347","authenticated-orcid":false,"given":"Nadilla Hafani","family":"Putri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9129-8951","authenticated-orcid":false,"given":"Ulfa Latifa","family":"Hanum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6934-9616","authenticated-orcid":false,"given":"Yossi","family":"Olivia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5196-0567","authenticated-orcid":false,"given":"Sanjida Afrin","family":"Rose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6729-8129","authenticated-orcid":false,"given":"Ki Moo","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"key":"4519_CR1","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s41060-025-00892-y","volume":"21","author":"MM Abdelsamie","year":"2025","unstructured":"Abdelsamie MM, Azab SS, Hefny HA (2025) Deep multi-task learning: a review of concepts, methods, and cross-domain applications. Int J Data Sci Anal 21:77. https:\/\/doi.org\/10.1007\/s41060-025-00892-y","journal-title":"Int J Data Sci Anal"},{"key":"4519_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.35248\/2157-7463.22.13.006","volume":"13","author":"I Arab","year":"2022","unstructured":"Arab I, Barakat K (2022) ToxTree: descriptor based machine learning models to predict hERG and Nav15 cardiotoxicity. J Pharm Pharm 13:1\u201316. https:\/\/doi.org\/10.35248\/2157-7463.22.13.006","journal-title":"J Pharm Pharm"},{"key":"4519_CR3","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1021\/acs.jcim.3c01301","volume":"64","author":"I Arab","year":"2024","unstructured":"Arab I, Egghe K, Laukens K et al (2024) Benchmarking of small molecule feature representations for hERG, Nav1.5, and Cav1.2 cardiotoxicity prediction. J Chem Inf Model 64:2515\u20132527. https:\/\/doi.org\/10.1021\/acs.jcim.3c01301","journal-title":"J Chem Inf Model"},{"key":"4519_CR4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento AP, Hersey A, F\u00e9lix E et al (2020) An open source chemical structure curation pipeline using RDKit. J Cheminform 12:51. https:\/\/doi.org\/10.1186\/s13321-020-00456-1","journal-title":"J Cheminform"},{"key":"4519_CR5","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1021\/jm301564f","volume":"56","author":"JFS Carvalho","year":"2013","unstructured":"Carvalho JFS, Louvel J, Doornbos MLJ et al (2013) Strategies to reduce hERG K+ channel blockade. Exploring heteroaromaticity and rigidity in novel pyridine analogues of dofetilide. J Med Chem 56:2828\u20132840. https:\/\/doi.org\/10.1021\/jm301564f","journal-title":"J Med Chem"},{"key":"4519_CR6","doi-asserted-by":"publisher","DOI":"10.1111\/cts.70098","volume":"17","author":"KW Chaudhary","year":"2024","unstructured":"Chaudhary KW, Clancy CE, Yang P-C et al (2024) An overview of drug-induced sodium channel blockade and changes in cardiac conduction: implications for drug safety. Clin Transl Sci 17:e70098. https:\/\/doi.org\/10.1111\/cts.70098","journal-title":"Clin Transl Sci"},{"key":"4519_CR7","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s10822-016-9898-z","volume":"30","author":"S Chavan","year":"2016","unstructured":"Chavan S, Abdelaziz A, Wiklander JG, Nicholls IA (2016) A k-nearest neighbor classification of hERG K+ channel blockers. J Comput Aided Mol des 30:229\u2013236. https:\/\/doi.org\/10.1007\/s10822-016-9898-z","journal-title":"J Comput Aided Mol des"},{"key":"4519_CR8","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1002\/jat.4477","volume":"43","author":"Y Chen","year":"2023","unstructured":"Chen Y, Yu X, Li W et al (2023) In silico prediction of hERG blockers using machine learning and deep learning approaches. J Appl Toxicol 43:1462","journal-title":"J Appl Toxicol"},{"key":"4519_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2024.134724","volume":"474","author":"Z Chen","year":"2024","unstructured":"Chen Z, Li N, Zhang P et al (2024) CardioDPi: an explainable deep-learning model for identifying cardiotoxic chemicals targeting hERG, Cav1.2, and Nav1.5 channels. J Hazard Mater 474:134724. https:\/\/doi.org\/10.1016\/j.jhazmat.2024.134724","journal-title":"J Hazard Mater"},{"key":"4519_CR10","doi-asserted-by":"publisher","first-page":"4829","DOI":"10.1111\/bph.15915","volume":"179","author":"AP Clark","year":"2022","unstructured":"Clark AP, Wei S, Kalola D et al (2022) An in silico\u2013in vitro pipeline for drug cardiotoxicity screening identifies ionic pro-arrhythmia mechanisms. Br J Pharmacol 179:4829\u20134843. https:\/\/doi.org\/10.1111\/bph.15915","journal-title":"Br J Pharmacol"},{"key":"4519_CR11","doi-asserted-by":"publisher","first-page":"4758","DOI":"10.1021\/acs.jcim.1c00744","volume":"61","author":"TM Creanza","year":"2021","unstructured":"Creanza TM, Delre P, Ancona N et al (2021) Structure-based prediction of hERG-related cardiotoxicity: a benchmark study. J Chem Inf Model 61:4758\u20134770. https:\/\/doi.org\/10.1021\/acs.jcim.1c00744","journal-title":"J Chem Inf Model"},{"key":"4519_CR12","doi-asserted-by":"publisher","first-page":"141","DOI":"10.2174\/1573403X12666160301120217","volume":"12","author":"LX Cubeddu","year":"2016","unstructured":"Cubeddu LX (2016) Drug-induced inhibition and trafficking disruption of ion channels: pathogenesis of QT abnormalities and drug-induced fatal arrhythmias. Curr Cardiol Rev 12:141\u2013154. https:\/\/doi.org\/10.2174\/1573403X12666160301120217","journal-title":"Curr Cardiol Rev"},{"key":"4519_CR13","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837\u2013845","journal-title":"Biometrics"},{"key":"4519_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.951083","author":"P Delre","year":"2022","unstructured":"Delre P, Lavado GJ, Lamanna G et al (2022) Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques. Front Pharmacol. https:\/\/doi.org\/10.3389\/fphar.2022.951083","journal-title":"Front Pharmacol"},{"key":"4519_CR15","doi-asserted-by":"publisher","first-page":"10194","DOI":"10.1021\/acs.jcim.5c01873","volume":"65","author":"HA Duy","year":"2025","unstructured":"Duy HA, Srisongkram T (2025) Toward explainable carcinogenicity prediction: an integrated cheminformatics approach and consensus framework for possibly carcinogenic chemicals. J Chem Inf Model 65:10194\u201310220. https:\/\/doi.org\/10.1021\/acs.jcim.5c01873","journal-title":"J Chem Inf Model"},{"key":"4519_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-99766-3","volume":"15","author":"G Falc\u00f3n-Cano","year":"2025","unstructured":"Falc\u00f3n-Cano G, Morales-Helguera A, Lambert H et al (2025) hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping. Sci Rep 15:15585. https:\/\/doi.org\/10.1038\/s41598-025-99766-3","journal-title":"Sci Rep"},{"key":"4519_CR17","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1021\/acsmedchemlett.5c00192","volume":"16","author":"S Feng","year":"2025","unstructured":"Feng S, Liang SH (2025) Novel sulfonamide derivatives as Nav1.5 sodium channel blockers for treating atrial fibrillation. ACS Med Chem Lett 16:748\u2013749. https:\/\/doi.org\/10.1021\/acsmedchemlett.5c00192","journal-title":"ACS Med Chem Lett"},{"key":"4519_CR18","doi-asserted-by":"publisher","first-page":"3517","DOI":"10.1021\/acs.jcim.5c00022","volume":"65","author":"L Feng","year":"2025","unstructured":"Feng L, Fu X, Du Z et al (2025) Multictox: empowering accurate cardiotoxicity prediction through adaptive multimodal learning. J Chem Inf Model 65:3517\u20133528. https:\/\/doi.org\/10.1021\/acs.jcim.5c00022","journal-title":"J Chem Inf Model"},{"key":"4519_CR19","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.842645","author":"TM Gamal El-Din","year":"2022","unstructured":"Gamal El-Din TM, Lenaeus MJ (2022) Fenestropathy of voltage-gated sodium channels. Front Pharmacol. https:\/\/doi.org\/10.3389\/fphar.2022.842645","journal-title":"Front Pharmacol"},{"key":"4519_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2020.112290","volume":"195","author":"A Garrido","year":"2020","unstructured":"Garrido A, Lepailleur A, Mignani SM et al (2020) hERG toxicity assessment: useful guidelines for drug design. Eur J Med Chem 195:112290. https:\/\/doi.org\/10.1016\/j.ejmech.2020.112290","journal-title":"Eur J Med Chem"},{"key":"4519_CR21","doi-asserted-by":"publisher","first-page":"630","DOI":"10.3390\/chemistry4030045","volume":"4","author":"H Goel","year":"2022","unstructured":"Goel H, Yu W, MacKerell AD Jr (2022) hERG blockade prediction by combining site identification by ligand competitive saturation and physicochemical properties. Chemistry 4:630\u2013646. https:\/\/doi.org\/10.3390\/chemistry4030045","journal-title":"Chemistry"},{"key":"4519_CR22","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1002\/jcc.23361","volume":"34","author":"P Gramatica","year":"2013","unstructured":"Gramatica P, Chirico N, Papa E et al (2013) Qsarin: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem 34:2121\u20132132. https:\/\/doi.org\/10.1002\/jcc.23361","journal-title":"J Comput Chem"},{"key":"4519_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-025-01039-8","volume":"17","author":"Q Guo","year":"2025","unstructured":"Guo Q, Hernandez-Hernandez S, Ballester PJ (2025) UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines*. J Cheminform 17:94. https:\/\/doi.org\/10.1186\/s13321-025-01039-8","journal-title":"J Cheminform"},{"key":"4519_CR24","doi-asserted-by":"publisher","first-page":"36595","DOI":"10.1038\/srep36595","volume":"6","author":"S-B He","year":"2016","unstructured":"He S-B, Ben H, Kuang Z-K et al (2016) Predicting subtype selectivity for adenosine receptor ligands with three-dimensional biologically relevant spectrum (BRS-3D). Sci Rep 6:36595. https:\/\/doi.org\/10.1038\/srep36595","journal-title":"Sci Rep"},{"key":"4519_CR25","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/164_2022_624","volume":"279","author":"KG Herold","year":"2023","unstructured":"Herold KG, Hussey JW, Dick IE (2023) CACNA1C-related channelopathies. Handb Exp Pharmacol 279:159\u2013181. https:\/\/doi.org\/10.1007\/164_2022_624","journal-title":"Handb Exp Pharmacol"},{"key":"4519_CR26","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2214700120","volume":"120","author":"A Jain","year":"2023","unstructured":"Jain A, Stack O, Ghodrati S et al (2023) KCNH2 encodes a nuclear-targeted polypeptide that mediates hERG1 channel gating and expression. Proc Natl Acad Sci 120:e2214700120. https:\/\/doi.org\/10.1073\/pnas.2214700120","journal-title":"Proc Natl Acad Sci"},{"key":"4519_CR27","doi-asserted-by":"publisher","first-page":"4365","DOI":"10.1016\/j.bmc.2013.04.054","volume":"21","author":"S Kang","year":"2013","unstructured":"Kang S, Cooper G, Dunne SF et al (2013) Antagonism of L-type Ca2+ channels CaV1.3 and CaV1.2 by 1,4-dihydropyrimidines and 4H-pyrans as dihydropyridine mimics. Bioorg Med Chem 21:4365\u20134373. https:\/\/doi.org\/10.1016\/j.bmc.2013.04.054","journal-title":"Bioorg Med Chem"},{"key":"4519_CR28","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1248\/cpb.59.1110","volume":"59","author":"Y Kawai","year":"2011","unstructured":"Kawai Y, Tsukamoto S, Ito J et al (2011) A risk assessment of human ether-a-go-go-related gene potassium channel inhibition by using lipophilicity and basicity for drug discovery. Chem Pharm Bull (Tokyo) 59:1110\u20131116. https:\/\/doi.org\/10.1248\/cpb.59.1110","journal-title":"Chem Pharm Bull (Tokyo)"},{"key":"4519_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21218099","volume":"21","author":"M Kowalska","year":"2020","unstructured":"Kowalska M, Nowaczyk J, Nowaczyk A (2020) KV11.1, NaV1.5, and CaV1.2 transporter proteins as antitarget for drug cardiotoxicity. Int J Mol Sci 21:8099. https:\/\/doi.org\/10.3390\/ijms21218099","journal-title":"Int J Mol Sci"},{"key":"4519_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccst.2025.100478","volume":"16","author":"J Lee","year":"2025","unstructured":"Lee J, Sun X, Errington E et al (2025) A KAN-based interpretable framework for prediction of global warming potential across chemical space. Carbon Capture Sci Technol 16:100478. https:\/\/doi.org\/10.1016\/j.ccst.2025.100478","journal-title":"Carbon Capture Sci Technol"},{"key":"4519_CR31","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1021\/acs.jcim.5c02465","volume":"66","author":"S Li","year":"2026","unstructured":"Li S, Sun P (2026) Toward more trustworthy QSAR: a systematic discussion on data set partitioning. J Chem Inf Model 66:2199\u20132210. https:\/\/doi.org\/10.1021\/acs.jcim.5c02465","journal-title":"J Chem Inf Model"},{"key":"4519_CR32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0324018","volume":"20","author":"C Liu","year":"2025","unstructured":"Liu C (2025) Targeted drug screening for autism based on Cav1.2 calcium ion channel. PLoS ONE 20:e0324018. https:\/\/doi.org\/10.1371\/journal.pone.0324018","journal-title":"PLoS ONE"},{"key":"4519_CR33","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1002\/pro.2763","volume":"24","author":"X Liu","year":"2015","unstructured":"Liu X, Yu H, Zhao X, Huang X-R (2015) Molecular simulations study of novel 1,4-dihydropyridines derivatives with a high selectivity for Cav3.1 calcium channel. Protein Sci 24:1737\u20131747. https:\/\/doi.org\/10.1002\/pro.2763","journal-title":"Protein Sci"},{"key":"4519_CR34","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H et al (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2:56\u201367. https:\/\/doi.org\/10.1038\/s42256-019-0138-9","journal-title":"Nat Mach Intell"},{"key":"4519_CR35","doi-asserted-by":"publisher","first-page":"11416","DOI":"10.1021\/acs.jmedchem.9b00937","volume":"62","author":"R Malek","year":"2019","unstructured":"Malek R, Arribas RL, Palomino-Antolin A et al (2019) New dual small molecules for Alzheimer\u2019s disease therapy combining histamine H3 receptor (H3R) antagonism and calcium channels blockade with additional cholinesterase inhibition. J Med Chem 62:11416\u201311422. https:\/\/doi.org\/10.1021\/acs.jmedchem.9b00937","journal-title":"J Med Chem"},{"key":"4519_CR36","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1038\/s41467-023-43502-w","volume":"15","author":"P Mesirca","year":"2024","unstructured":"Mesirca P, Chemin J, Barr\u00e8re C et al (2024) Selective blockade of Cav1.2 (\u03b11C) versus Cav1.3 (\u03b11D) L-type calcium channels by the black mamba toxin calciseptine. Nat Commun 15:54","journal-title":"Nat Commun"},{"key":"4519_CR37","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1039\/c1ra00131k","volume":"1","author":"NSHN Moorthy","year":"2011","unstructured":"Moorthy NSHN, Ramos MJ, Fernandes PA (2011) hERG binding feature analysis of structurally diverse compounds by QSAR and fragmental analysis. RSC Adv 1:1126. https:\/\/doi.org\/10.1039\/c1ra00131k","journal-title":"RSC Adv"},{"key":"4519_CR38","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0258-y","volume":"10","author":"H Moriwaki","year":"2018","unstructured":"Moriwaki H, Tian Y-S, Kawashita N, Takagi T (2018) Mordred: a molecular descriptor calculator. J Cheminform 10:4. https:\/\/doi.org\/10.1186\/s13321-018-0258-y","journal-title":"J Cheminform"},{"key":"4519_CR39","doi-asserted-by":"publisher","first-page":"107776","DOI":"10.1016\/j.jmgm.2020.107776","volume":"102","author":"FES Mosa","year":"2021","unstructured":"Mosa FES, Suryanarayanan C, Feng T, Barakat K (2021) Effects of selective calcium channel blockers on ions\u2019 permeation through the human Cav1.2 ion channel: a Computational study. J Mol Graph Modell 102:107776. https:\/\/doi.org\/10.1016\/j.jmgm.2020.107776","journal-title":"J Mol Graph Modell"},{"key":"4519_CR40","doi-asserted-by":"publisher","first-page":"1924","DOI":"10.1021\/acs.chemrestox.4c00186","volume":"37","author":"Z Mou","year":"2024","unstructured":"Mou Z, Volarath P, Racz R et al (2024) Quantitative structure-activity relationship models to predict cardiac adverse effects. Chem Res Toxicol 37:1924\u20131933. https:\/\/doi.org\/10.1021\/acs.chemrestox.4c00186","journal-title":"Chem Res Toxicol"},{"key":"4519_CR41","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6c00357","author":"FS Nursyafi","year":"2026","unstructured":"Nursyafi FS, Pramudito MA, Fuadah YN et al (2026a) ToxCML: a hybrid mfCoQ-RASAR-based platform integrating consensus QSAR and read-across for comprehensive multi-end point toxicity assessment. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.6c00357","journal-title":"J Chem Inf Model"},{"key":"4519_CR42","doi-asserted-by":"publisher","DOI":"10.1080\/15376516.2026.2643659","author":"FS Nursyafi","year":"2026","unstructured":"Nursyafi FS, Pramudito MA, NurFuadah Y (2026b) Lim KM (2026b) Interpretable multi-modality consensus QSAR framework: integrating machine and deep learning for enhanced multi-endpoint toxicity assessment. Toxicol Mech Methods. https:\/\/doi.org\/10.1080\/15376516.2026.2643659","journal-title":"Toxicol Mech Methods"},{"key":"4519_CR43","doi-asserted-by":"publisher","DOI":"10.1787\/9789264085442-en","volume-title":"Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models","author":"OECD","year":"2014","unstructured":"OECD (2014) Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models. OECD"},{"key":"4519_CR44","doi-asserted-by":"publisher","DOI":"10.1002\/minf.202400265","volume":"44","author":"AA Orlov","year":"2025","unstructured":"Orlov AA, Akhmetshin TN, Horvath D et al (2025) From high dimensions to human insight: exploring dimensionality reduction for chemical space visualization. Mol Inform 44:e202400265. https:\/\/doi.org\/10.1002\/minf.202400265","journal-title":"Mol Inform"},{"key":"4519_CR45","doi-asserted-by":"publisher","DOI":"10.3389\/fchem.2022.852893","volume":"10","author":"\u00c1 Orosz","year":"2022","unstructured":"Orosz \u00c1, H\u00e9berger K, R\u00e1cz A (2022) Comparison of descriptor- and fingerprint sets in machine learning models for ADME-Tox targets. Front Chem 10:852893. https:\/\/doi.org\/10.3389\/fchem.2022.852893","journal-title":"Front Chem"},{"key":"4519_CR46","doi-asserted-by":"publisher","DOI":"10.1111\/cts.70056","volume":"17","author":"AV Ponce-Bobadilla","year":"2024","unstructured":"Ponce-Bobadilla AV, Schmitt V, Maier CS et al (2024) Practical guide to SHAP analysis: explaining supervised machine learning model predictions in drug development. Clin Transl Sci 17:e70056. https:\/\/doi.org\/10.1111\/cts.70056","journal-title":"Clin Transl Sci"},{"key":"4519_CR47","unstructured":"Putria NH, Arisanty D, Nursyafi FS et al (2025) The pro-apoptotic potential of Sungkai leaves (Peronema Canescens Jack) in breast cancer via network pharmacology and molecular docking analysis"},{"key":"4519_CR48","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-56706-x","volume":"14","author":"O Rainio","year":"2024","unstructured":"Rainio O, Teuho J, Kl\u00e9n R (2024) Evaluation metrics and statistical tests for machine learning. Sci Rep 14:6086. https:\/\/doi.org\/10.1038\/s41598-024-56706-x","journal-title":"Sci Rep"},{"key":"4519_CR49","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.ccep.2014.07.003","volume":"6","author":"DM Roden","year":"2014","unstructured":"Roden DM (2014) Pharmacology and toxicology of Nav1.5-class 1 anti-arrhythmic drugs. Card Electrophysiol Clin 6:695\u2013704. https:\/\/doi.org\/10.1016\/j.ccep.2014.07.003","journal-title":"Card Electrophysiol Clin"},{"key":"4519_CR50","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s10822-020-00314-0","volume":"34","author":"R Rodr\u00edguez-P\u00e9rez","year":"2020","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J (2020) Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J Comput Aided Mol des 34:1013\u20131026. https:\/\/doi.org\/10.1007\/s10822-020-00314-0","journal-title":"J Comput Aided Mol des"},{"key":"4519_CR51","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1161\/CIRCRESAHA.120.316901","volume":"126","author":"Y Rudy","year":"2020","unstructured":"Rudy Y (2020) In silico pipeline for drug cardiotoxicity assessment. Circ Res 126:965\u2013967. https:\/\/doi.org\/10.1161\/CIRCRESAHA.120.316901","journal-title":"Circ Res"},{"key":"4519_CR52","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.1093\/bioinformatics\/btaa075","volume":"36","author":"JY Ryu","year":"2020","unstructured":"Ryu JY, Lee MY, Lee JH et al (2020) DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity. Bioinformatics 36:3049\u20133055. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa075","journal-title":"Bioinformatics"},{"key":"4519_CR53","unstructured":"Ryu S, Lim J, Hong SH, Kim WY (2018) Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network"},{"key":"4519_CR54","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab365","volume":"23","author":"MV Sabando","year":"2022","unstructured":"Sabando MV, Ponzoni I, Milios EE, Soto AJ (2022) Using molecular embeddings in QSAR modeling: does it make a difference? Brief Bioinform 23:bbab365. https:\/\/doi.org\/10.1093\/bib\/bbab365","journal-title":"Brief Bioinform"},{"key":"4519_CR55","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1021\/acs.chemrestox.3c00400","volume":"37","author":"IH Sanches","year":"2024","unstructured":"Sanches IH, Braga RC, Alves VM, Andrade CH (2024) Enhancing hERG risk assessment with interpretable classificatory and regression models. Chem Res Toxicol 37:910\u2013922. https:\/\/doi.org\/10.1021\/acs.chemrestox.3c00400","journal-title":"Chem Res Toxicol"},{"key":"4519_CR56","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1021\/ci100062n","volume":"50","author":"M Sastry","year":"2010","unstructured":"Sastry M, Lowrie JF, Dixon SL, Sherman W (2010) Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J Chem Inf Model 50:771\u2013784. https:\/\/doi.org\/10.1021\/ci100062n","journal-title":"J Chem Inf Model"},{"key":"4519_CR57","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10822-020-00361-7","volume":"35","author":"A Sato","year":"2021","unstructured":"Sato A, Miyao T, Jasial S, Funatsu K (2021) Comparing predictive ability of QSAR\/QSPR models using 2D and 3D molecular representations. J Comput Aided Mol des 35:179\u2013193. https:\/\/doi.org\/10.1007\/s10822-020-00361-7","journal-title":"J Comput Aided Mol des"},{"key":"4519_CR58","doi-asserted-by":"publisher","DOI":"10.1038\/srep24182","volume":"6","author":"P Saxena","year":"2016","unstructured":"Saxena P, Zangerl-Plessl E-M, Linder T et al (2016) New potential binding determinant for hERG channel inhibitors. Sci Rep 6:24182. https:\/\/doi.org\/10.1038\/srep24182","journal-title":"Sci Rep"},{"key":"4519_CR59","doi-asserted-by":"publisher","unstructured":"Seal S, Spjuth O, Hosseini-Gerami L, et al (2023) Insights into drug cardiotoxicity from biological and chemical data: the first public classifiers for FDA DICTrank. bioRxiv 2023.10.15.562398. https:\/\/doi.org\/10.1101\/2023.10.15.562398","DOI":"10.1101\/2023.10.15.562398"},{"key":"4519_CR60","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/203518","volume":"2014","author":"MA Shaldam","year":"2014","unstructured":"Shaldam MA, Elhamamsy MH, Esmat EA, El-Moselhy TF (2014) 1,4-dihydropyridine calcium channel blockers: homology modeling of the receptor and assessment of structure activity relationship. Int Sch Res Not 2014:203518. https:\/\/doi.org\/10.1155\/2014\/203518","journal-title":"Int Sch Res Not"},{"key":"4519_CR61","doi-asserted-by":"publisher","first-page":"3423","DOI":"10.1039\/D1RA07956E","volume":"12","author":"M Shan","year":"2022","unstructured":"Shan M, Jiang C, Chen J et al (2022) Predicting hERG channel blockers with directed message passing neural networks. RSC Adv 12:3423\u20133430. https:\/\/doi.org\/10.1039\/D1RA07956E","journal-title":"RSC Adv"},{"key":"4519_CR62","doi-asserted-by":"publisher","first-page":"4834","DOI":"10.1016\/j.bmc.2006.03.019","volume":"14","author":"HZ Si","year":"2006","unstructured":"Si HZ, Wang T, Zhang KJ et al (2006) Qsar study of 1,4-dihydropyridine calcium channel antagonists based on gene expression programming. Bioorg Med Chem 14:4834\u20134841. https:\/\/doi.org\/10.1016\/j.bmc.2006.03.019","journal-title":"Bioorg Med Chem"},{"key":"4519_CR63","doi-asserted-by":"publisher","first-page":"9027","DOI":"10.1021\/acs.jcim.4c00863","volume":"64","author":"J Sieg","year":"2024","unstructured":"Sieg J, Feldmann CW, Hemmerich J et al (2024) MolPipeline: a Python package for processing molecules with RDKit in Scikit-learn. J Chem Inf Model 64:9027\u20139033. https:\/\/doi.org\/10.1021\/acs.jcim.4c00863","journal-title":"J Chem Inf Model"},{"key":"4519_CR64","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s13321-021-00576-2","volume":"13","author":"J Simm","year":"2021","unstructured":"Simm J, Humbeck L, Zalewski A et al (2021) Splitting chemical structure data sets for federated privacy-preserving machine learning. J Cheminform 13:96. https:\/\/doi.org\/10.1186\/s13321-021-00576-2","journal-title":"J Cheminform"},{"key":"4519_CR65","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3389\/fphar.2018.00074","volume":"9","author":"RS Sim\u00f5es","year":"2018","unstructured":"Sim\u00f5es RS, Maltarollo VG, Oliveira PR, Honorio KM (2018) Transfer and multi-task learning in QSAR modeling: advances and challenges. Front Pharmacol 9:74. https:\/\/doi.org\/10.3389\/fphar.2018.00074","journal-title":"Front Pharmacol"},{"key":"4519_CR66","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201800108","volume":"38","author":"S Sosnin","year":"2019","unstructured":"Sosnin S, Vashurina M, Withnall M et al (2019) A survey of multi\u2010task learning methods in chemoinformatics. Mol Inform 38:1800108. https:\/\/doi.org\/10.1002\/minf.201800108","journal-title":"Mol Inform"},{"key":"4519_CR67","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1186\/1752-153X-7-167","volume":"7","author":"C Springer","year":"2013","unstructured":"Springer C, Sokolnicki KL (2013) A fingerprint pair analysis of hERG inhibition data. Chem Cent J 7:167. https:\/\/doi.org\/10.1186\/1752-153X-7-167","journal-title":"Chem Cent J"},{"key":"4519_CR68","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.gene.2015.08.062","volume":"573","author":"CC Veerman","year":"2015","unstructured":"Veerman CC, Wilde AAM, Lodder EM (2015) The cardiac sodium channel gene SCN5A and its gene product NaV1.5: role in physiology and pathophysiology. Gene 573:177\u2013187. https:\/\/doi.org\/10.1016\/j.gene.2015.08.062","journal-title":"Gene"},{"key":"4519_CR69","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1002\/cpt.896","volume":"103","author":"J Vicente","year":"2018","unstructured":"Vicente J, Zusterzeel R, Johannesen L et al (2018) Mechanistic model-informed proarrhythmic risk assessment of drugs: review of the \u201cCiPA\u201d initiative and design of a prospective clinical validation study. Clin Pharmacol Ther 103:54\u201366. https:\/\/doi.org\/10.1002\/cpt.896","journal-title":"Clin Pharmacol Ther"},{"key":"4519_CR70","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1186\/s13321-025-01072-7","volume":"17","author":"EL Vigan\u00f2","year":"2025","unstructured":"Vigan\u00f2 EL, Iwan M, Colombo E et al (2025) Mixture of experts for multitask learning in cardiotoxicity assessment. J Cheminform 17:135. https:\/\/doi.org\/10.1186\/s13321-025-01072-7","journal-title":"J Cheminform"},{"key":"4519_CR71","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1002\/minf.201300030","volume":"33","author":"S Vorberg","year":"2014","unstructured":"Vorberg S, Tetko IV (2014) Modeling the biodegradability of chemical compounds using the online CHEmical modeling environment (OCHEM). Mol Inform 33:73\u201385. https:\/\/doi.org\/10.1002\/minf.201300030","journal-title":"Mol Inform"},{"key":"4519_CR72","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijbiomac.2024.133825","volume":"276","author":"T Wang","year":"2024","unstructured":"Wang T, Du Z, Zhuo L et al (2024) Multicblo: enhancing predictions of compound-induced inhibition of cardiac ion channels with advanced multimodal learning. Int J Biol Macromol 276:133825. https:\/\/doi.org\/10.1016\/j.ijbiomac.2024.133825","journal-title":"Int J Biol Macromol"},{"key":"4519_CR73","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1016\/j.bmcl.2006.12.061","volume":"17","author":"MJ Waring","year":"2007","unstructured":"Waring MJ, Johnstone C (2007) A quantitative assessment of hERG liability as a function of lipophilicity. Bioorg Med Chem Lett 17:1759\u20131764. https:\/\/doi.org\/10.1016\/j.bmcl.2006.12.061","journal-title":"Bioorg Med Chem Lett"},{"key":"4519_CR74","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-47116-8","volume":"15","author":"Y Wei","year":"2024","unstructured":"Wei Y, Yu Z, Wang L et al (2024) Structural bases of inhibitory mechanism of CaV1.2 channel inhibitors. Nat Commun 15:2772. https:\/\/doi.org\/10.1038\/s41467-024-47116-8","journal-title":"Nat Commun"},{"key":"4519_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpha.2025.101411","volume":"15","author":"C Xu","year":"2025","unstructured":"Xu C, Xu Y, Hu Z et al (2025) Unveiling optimal molecular features for hERG insights with automatic machine learning. J Pharm Anal 15:101411. https:\/\/doi.org\/10.1016\/j.jpha.2025.101411","journal-title":"J Pharm Anal"},{"key":"4519_CR76","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1021\/mp050027v","volume":"2","author":"X Yao","year":"2005","unstructured":"Yao X, Liu H, Zhang R et al (2005) Qsar and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. Mol Pharm 2:348\u2013356. https:\/\/doi.org\/10.1021\/mp050027v","journal-title":"Mol Pharm"},{"key":"4519_CR77","doi-asserted-by":"publisher","first-page":"1830","DOI":"10.1021\/acs.jcim.2c00256","volume":"62","author":"X Zhang","year":"2022","unstructured":"Zhang X, Mao J, Wei M et al (2022) Hergspred: accurate classification of hERG blockers\/nonblockers with machine-learning models. J Chem Inf Model 62:1830\u20131839. https:\/\/doi.org\/10.1021\/acs.jcim.2c00256","journal-title":"J Chem Inf Model"},{"key":"4519_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103484","volume":"108","author":"Z Zhao","year":"2020","unstructured":"Zhao Z, Qin J, Gou Z et al (2020) Multi-task learning models for predicting active compounds. J Biomed Inform 108:103484. https:\/\/doi.org\/10.1016\/j.jbi.2020.103484","journal-title":"J Biomed Inform"}],"container-title":["Archives of Toxicology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00204-026-04519-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00204-026-04519-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00204-026-04519-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T21:54:13Z","timestamp":1786485253000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00204-026-04519-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,11]]},"references-count":78,"alternative-id":["4519"],"URL":"https:\/\/doi.org\/10.1007\/s00204-026-04519-4","relation":{},"ISSN":["0340-5761","1432-0738"],"issn-type":[{"value":"0340-5761","type":"print"},{"value":"1432-0738","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,11]]},"assertion":[{"value":"2 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"K.M.L. and A.L.F is employed by Meta Heart Inc. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The manuscript does not contain newly generated clinical studies or patient data.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}}]}}