{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T11:14:02Z","timestamp":1788002042503,"version":"build-2784847793"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["252300421626"],"award-info":[{"award-number":["252300421626"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018925","name":"Health Commission of Henan Province","doi-asserted-by":"publisher","award":["2024ZXZX1180"],"award-info":[{"award-number":["2024ZXZX1180"]}],"id":[{"id":"10.13039\/100018925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018551","name":"Science and Technology Innovation Talents in Universities of Henan Province","doi-asserted-by":"publisher","award":["23IRTSTHN026"],"award-info":[{"award-number":["23IRTSTHN026"]}],"id":[{"id":"10.13039\/501100018551","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017634","name":"Henan University of Chinese Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100017634","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computational Biology and Chemistry"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1016\/j.compbiolchem.2025.108622","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T22:57:19Z","timestamp":1754521039000},"page":"108622","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"P1","title":["FEAOF: A transferable framework applied to prediction of hERG-related cardiotoxicity"],"prefix":"10.1016","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7745-3372","authenticated-orcid":false,"given":"Bowen","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghui","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqi","family":"Huo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compbiolchem.2025.108622_bib1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"issue":"7","key":"10.1016\/j.compbiolchem.2025.108622_bib2","doi-asserted-by":"crossref","first-page":"2515","DOI":"10.1021\/acs.jcim.3c01301","article-title":"Benchmarking of small molecule feature representations for hERG, Nav1. 5, and Cav1. 2 cardiotoxicity prediction","volume":"64","author":"Arab","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.vascn.2021.107072","article-title":"Use of automated patch clamp in cardiac safety assessment: past, present and future perspectives","volume":"110","author":"Bell","year":"2021","journal-title":"J. Pharmacol. Toxicol. Methods"},{"key":"10.1016\/j.compbiolchem.2025.108622_bib4","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/A:1018054314350","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib5","unstructured":"Chithrananda, S.; Grand, G.; Ramsundar, B., ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885 2020."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib6","series-title":"An introduction to support vector machines and other kernel-based learning methods","author":"Cristianini","year":"2000"},{"key":"10.1016\/j.compbiolchem.2025.108622_bib7","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1007\/s10822-016-9986-0","article-title":"Compilation and physicochemical classification analysis of a diverse hERG inhibition database","volume":"30","author":"Didziapetris","year":"2016","journal-title":"J. Comput. Aided Mol. Des."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-018-0270-2","article-title":"PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions","volume":"10","author":"Dong","year":"2018","journal-title":"J. Chemin.."},{"issue":"6","key":"10.1016\/j.compbiolchem.2025.108622_bib9","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1021\/ci010132r","article-title":"Reoptimization of MDL keys for use in drug discovery","volume":"42","author":"Durant","year":"2002","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib10","unstructured":"Fey, M.; Lenssen, J.E., Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428 2019."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib11","first-page":"1189","article-title":"Greedy function approximation: a gradient boosting machine","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"issue":"D1","key":"10.1016\/j.compbiolchem.2025.108622_bib12","doi-asserted-by":"crossref","first-page":"D1417","DOI":"10.1093\/nar\/gkab1013","article-title":"VARIDT 2.0: structural variability of drug transporter","volume":"50","author":"Fu","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.trac.2020.116082","article-title":"Application of electrophysiological technique in toxicological study: from manual to automated patch-clamp recording","volume":"133","author":"Gao","year":"2020","journal-title":"TrAC Trends Anal. Chem."},{"issue":"22","key":"10.1016\/j.compbiolchem.2025.108622_bib14","doi-asserted-by":"crossref","first-page":"3219","DOI":"10.1016\/0040-4020(80)80168-2","article-title":"Iterative partial equalization of orbital electronegativity\u2014a rapid access to atomic charges","volume":"36","author":"Gasteiger","year":"1980","journal-title":"Tetrahedron"},{"issue":"D1","key":"10.1016\/j.compbiolchem.2025.108622_bib15","doi-asserted-by":"crossref","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","article-title":"ChEMBL: a large-scale bioactivity database for drug discovery","volume":"40","author":"Gaulton","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib16","series-title":"International conference on machine learning","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"issue":"2","key":"10.1016\/j.compbiolchem.2025.108622_bib17","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1002\/med.21445","article-title":"Development of safe drugs: the hERG challenge","volume":"38","author":"Kalyaanamoorthy","year":"2018","journal-title":"Med. Res. Rev."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib18","doi-asserted-by":"crossref","DOI":"10.3389\/fmolb.2023.1121964","article-title":"Role of LGMN in tumor development and its progression and connection with the tumor microenvironment","volume":"10","author":"Khan","year":"2023","journal-title":"Front. Mol. Biosci."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib19","article-title":"Maxsmi: maximizing molecular property prediction performance with confidence estimation using smiles augmentation and deep learning","volume":"1","author":"Kimber","year":"2021","journal-title":"Artif. Intell. Life Sci."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib20","unstructured":"Kipf, T.N.; Welling, M., Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 2016."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib21","unstructured":"Landrum, G., Rdkit: open-source cheminformatics \u3008http:\/\/www.rdkit.org\u3009. Google Scholar There is no corresponding record for this reference 2016, 3 (8)."},{"issue":"6","key":"10.1016\/j.compbiolchem.2025.108622_bib22","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.3390\/molecules25061375","article-title":"A review on applications of computational methods in drug screening and design","volume":"25","author":"Lin","year":"2020","journal-title":"Molecules"},{"issue":"7","key":"10.1016\/j.compbiolchem.2025.108622_bib23","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1080\/17425255.2024.2377593","article-title":"Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study","volume":"20","author":"Liu","year":"2024","journal-title":"Expert Opin. Drug Metab. Toxicol."},{"issue":"5","key":"10.1016\/j.compbiolchem.2025.108622_bib24","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1002\/wcms.1183","article-title":"Machine learning methods in chemoinformatics","volume":"4","author":"Mitchell","year":"2014","journal-title":"Wiley Interdisciplinary Reviews Computational Molecular Science"},{"key":"10.1016\/j.compbiolchem.2025.108622_bib25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-018-0258-y","article-title":"Mordred: a molecular descriptor calculator","volume":"10","author":"Moriwaki","year":"2018","journal-title":"J. Chemin.."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib26","first-page":"32","article-title":"Pytorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib27","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"10.1016\/j.compbiolchem.2025.108622_bib28","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1002\/med.20019","article-title":"QT prolongation through hERG K+ channel blockade: current knowledge and strategies for the early prediction during drug development","volume":"25","author":"Recanatini","year":"2005","journal-title":"Med. Res. Rev."},{"issue":"15","key":"10.1016\/j.compbiolchem.2025.108622_bib29","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"2","key":"10.1016\/j.compbiolchem.2025.108622_bib30","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/0092-8674(95)90340-2","article-title":"A mechanistic link between an inherited and an acquird cardiac arrthytmia: HERG encodes the IKr potassium channel","volume":"81","author":"Sanguinetti","year":"1995","journal-title":"Cell"},{"issue":"2","key":"10.1016\/j.compbiolchem.2025.108622_bib31","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1002\/prot.21400","article-title":"Drug block of the hERG potassium channel: insight from modeling","volume":"68","author":"Stansfeld","year":"2007","journal-title":"Protein. Struct. Funct. Bioinforma."},{"issue":"2","key":"10.1016\/j.compbiolchem.2025.108622_bib32","doi-asserted-by":"crossref","DOI":"10.1002\/wcms.1568","article-title":"Machine learning in drug design: use of artificial intelligence to explore the chemical structure\u2013biological activity relationship","volume":"12","author":"Staszak","year":"2022","journal-title":"Wiley Interdisciplinary Reviews Computational Molecular Science"},{"issue":"28","key":"10.1016\/j.compbiolchem.2025.108622_bib33","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.2174\/092986707782794087","article-title":"Predictive models for HERG channel blockers: ligand-based and structure-based approaches","volume":"14","author":"Thai","year":"2007","journal-title":"Curr. Med. Chem."},{"issue":"11","key":"10.1016\/j.compbiolchem.2025.108622_bib34","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"23","key":"10.1016\/j.compbiolchem.2025.108622_bib35","doi-asserted-by":"crossref","first-page":"5938","DOI":"10.1021\/acs.jcim.2c01073","article-title":"Exposing the limitations of molecular machine learning with activity cliffs","volume":"62","author":"Van Tilborg","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compbiolchem.2025.108622_bib36","unstructured":"Vaswani, A., Attention is all you need. Advances in Neural Information Processing Systems 2017."},{"issue":"5","key":"10.1016\/j.compbiolchem.2025.108622_bib37","doi-asserted-by":"crossref","DOI":"10.1002\/wcms.1603","article-title":"A review of molecular representation in the age of machine learning","volume":"12","author":"Wigh","year":"2022","journal-title":"Wiley Interdisciplinary Reviews Computational Molecular Science"}],"container-title":["Computational Biology and Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S147692712500283X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S147692712500283X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T13:22:37Z","timestamp":1772889757000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S147692712500283X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":37,"alternative-id":["S147692712500283X"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2025.108622","relation":{},"ISSN":["1476-9271"],"issn-type":[{"value":"1476-9271","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FEAOF: A transferable framework applied to prediction of hERG-related cardiotoxicity","name":"articletitle","label":"Article Title"},{"value":"Computational Biology and Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2025.108622","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108622"}}