{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T12:05:53Z","timestamp":1785153953345,"version":"3.55.0"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100007540","name":"Jiangsu Provincial Agricultural Science and Technology Innovation Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007540","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Jiangxi Provincial Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110899","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T19:49:29Z","timestamp":1782935369000},"page":"110899","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["VBMA-DTA: A multimodal drug\u2013target binding affinity prediction framework via virtual bridging and multi-attention mechanism"],"prefix":"10.1016","volume":"126","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4790-5973","authenticated-orcid":false,"given":"Ming","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0792-7792","authenticated-orcid":false,"given":"Min","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyi","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianhua","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.bspc.2026.110899_b1","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TCBB.2022.3205282","article-title":"Modality-DTA: Multimodality fusion strategy for drug\u2013target affinity prediction","volume":"20","author":"Yang","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"1","key":"10.1016\/j.bspc.2026.110899_b2","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2026.110899_b3","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s12272-016-0791-z","article-title":"Target identification for biologically active small molecules using chemical biology approaches","volume":"39","author":"Lee","year":"2016","journal-title":"Arch. Pharmacal Res."},{"issue":"1","key":"10.1016\/j.bspc.2026.110899_b4","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.drudis.2015.08.001","article-title":"Identifying compound efficacy targets in phenotypic drug discovery","volume":"21","author":"Schirle","year":"2016","journal-title":"Drug Discov. Today"},{"issue":"10","key":"10.1016\/j.bspc.2026.110899_b5","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.7150\/ijbs.24612","article-title":"Review of drug repositioning approaches and resources","volume":"14","author":"Xue","year":"2018","journal-title":"Int. J. Biol. Sci."},{"issue":"6","key":"10.1016\/j.bspc.2026.110899_b6","doi-asserted-by":"crossref","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"},{"key":"10.1016\/j.bspc.2026.110899_b7","series-title":"Bioinformatics","first-page":"291","article-title":"Chapter 18 - molecular docking and molecular dynamics simulation","author":"Singh","year":"2022"},{"issue":"2","key":"10.1016\/j.bspc.2026.110899_b8","doi-asserted-by":"crossref","first-page":"bbad047","DOI":"10.1093\/bib\/bbad047","article-title":"Dockey: a modern integrated tool for large-scale molecular docking and virtual screening","volume":"24","author":"Du","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"16","key":"10.1016\/j.bspc.2026.110899_b9","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1002\/jcc.21256","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility","volume":"30","author":"Morris","year":"2009","journal-title":"J. Comput. Chem.: Org. Inorg. Phys. Biological"},{"issue":"5","key":"10.1016\/j.bspc.2026.110899_b10","doi-asserted-by":"crossref","DOI":"10.3390\/molecules25051030","article-title":"Protein X-ray crystallography and drug discovery","volume":"25","author":"Maveyraud","year":"2020","journal-title":"Molecules"},{"issue":"6","key":"10.1016\/j.bspc.2026.110899_b11","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1021\/ct800011m","article-title":"Perspective on free-energy perturbation calculations for chemical equilibria","volume":"4","author":"Jorgensen","year":"2008","journal-title":"J. Chem. Theory Comput.: JCTC"},{"issue":"6","key":"10.1016\/j.bspc.2026.110899_b12","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1093\/bib\/bbz143","article-title":"Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets","volume":"21","author":"\u00c7\u0131naroglu S S","year":"2020","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.bspc.2026.110899_b13","doi-asserted-by":"crossref","first-page":"bbab476","DOI":"10.1093\/bib\/bbab476","article-title":"Artificial intelligence in the prediction of protein\u2013ligand interactions: recent advances and future directions","volume":"23","author":"Dhakal","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"2","key":"10.1016\/j.bspc.2026.110899_b14","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/bib\/bbu010","article-title":"Toward more realistic drug\u2013target interaction predictions","volume":"16","author":"Pahikkala","year":"2014","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.bspc.2026.110899_b15","doi-asserted-by":"crossref","DOI":"10.1186\/s13321-017-0209-z","article-title":"SimBoost: a read-across approach for predicting drug\u2013target binding affinities using gradient boosting machines","volume":"9","author":"He","year":"2017","journal-title":"J. Cheminformatics"},{"issue":"3","key":"10.1016\/j.bspc.2026.110899_b16","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/JBHI.2023.3334239","article-title":"Hisif-DTA: A hierarchical semantic information fusion framework for drug-target affinity prediction","volume":"29","author":"Bi","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"6","key":"10.1016\/j.bspc.2026.110899_b17","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1109\/TCBBIO.2025.3563504","article-title":"PGDTA: Predicting drug-target affinity using three-dimensional structure of protein pocket and graph neural network","volume":"22","author":"Li","year":"2025","journal-title":"IEEE Trans. Comput. Biol. Bioinform."},{"key":"10.1016\/j.bspc.2026.110899_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102983","article-title":"SSR-DTA: Substructure-aware multi-layer graph neural networks for drug\u2013target binding affinity prediction","volume":"157","author":"Liu","year":"2024","journal-title":"Artif. Intell. Med."},{"issue":"17","key":"10.1016\/j.bspc.2026.110899_b19","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: deep drug\u2013target binding affinity prediction","volume":"34","author":"\u00d6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.bspc.2026.110899_b20","doi-asserted-by":"crossref","DOI":"10.1186\/s13321-022-00591-x","article-title":"ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding","volume":"14","author":"Wang","year":"2022","journal-title":"J. Cheminformatics"},{"key":"10.1016\/j.bspc.2026.110899_b21","series-title":"ELECTRA: Pre-training text encoders as discriminators rather than generators","author":"Clark","year":"2020"},{"issue":"2","key":"10.1016\/j.bspc.2026.110899_b22","doi-asserted-by":"crossref","first-page":"btad056","DOI":"10.1093\/bioinformatics\/btad056","article-title":"MFR-DTA: a multi-functional and robust model for predicting drug\u2013target binding affinity and region","volume":"39","author":"Hua","year":"2023","journal-title":"Bioinformatics"},{"issue":"13","key":"10.1016\/j.bspc.2026.110899_b23","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1021\/acs.jcim.4c00310","article-title":"MDF-DTA: A multi-dimensional fusion approach for drug-target binding affinity prediction","volume":"64","author":"Ranjan","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.bspc.2026.110899_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102640","article-title":"DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks","volume":"144","author":"Zhong","year":"2023","journal-title":"Artif. Intell. Med."},{"issue":"8","key":"10.1016\/j.bspc.2026.110899_b25","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"GraphDTA: predicting drug\u2013target binding affinity with graph neural networks","volume":"37","author":"Nguyen","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.bspc.2026.110899_b26","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.bspc.2026.110899_b27","series-title":"How powerful are graph neural networks?","author":"Xu","year":"2019"},{"key":"10.1016\/j.bspc.2026.110899_b28","series-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"10.1016\/j.bspc.2026.110899_b29","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1039\/D1SC05180F","article-title":"MGraphDTA: deep multiscale graph neural network for explainable drug\u2013target binding affinity prediction","volume":"13","author":"Yang","year":"2022","journal-title":"Chem. Sci."},{"key":"10.1016\/j.bspc.2026.110899_b30","doi-asserted-by":"crossref","first-page":"20701","DOI":"10.1039\/D0RA02297G","article-title":"Drug\u2013target affinity prediction using graph neural network and contact maps","volume":"10","author":"Jiang","year":"2020","journal-title":"RSC Adv."},{"key":"10.1016\/j.bspc.2026.110899_b31","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.neunet.2023.11.018","article-title":"AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism","volume":"169","author":"Wu","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.bspc.2026.110899_b32","series-title":"A generalization of transformer networks to graphs","author":"Dwivedi","year":"2021"},{"issue":"7","key":"10.1016\/j.bspc.2026.110899_b33","doi-asserted-by":"crossref","first-page":"2878","DOI":"10.1021\/acs.jcim.3c00866","article-title":"MMDTA: A multimodal deep model for drug-target affinity with a hybrid fusion strategy","volume":"64","author":"Zhong","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.bspc.2026.110899_b34","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s11042-009-0344-2","article-title":"Multimodal information fusion application to human emotion recognition from face and speech","volume":"49","author":"Mansoorizadeh","year":"2010","journal-title":"Multimedia Tools Appl."},{"issue":"6","key":"10.1016\/j.bspc.2026.110899_b35","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1109\/TCBB.2024.3451985","article-title":"MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction","volume":"21","author":"Zhang","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"3","key":"10.1016\/j.bspc.2026.110899_b36","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1109\/JBHI.2024.3386815","article-title":"Multimodal drug target binding affinity prediction using graph local substructure","volume":"29","author":"Peng","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110899_b37","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1039\/D1SC05180F","article-title":"MGraphDTA: deep multiscale graph neural network for explainable drug\u2013target binding affinity prediction","volume":"13","author":"Yang","year":"2022","journal-title":"Chem. Sci."},{"key":"10.1016\/j.bspc.2026.110899_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2021.128817","article-title":"Introducing block design in graph neural networks for molecular properties prediction","volume":"414","author":"Li","year":"2021","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.bspc.2026.110899_b39","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1038\/nbt.1990","article-title":"Comprehensive analysis of kinase inhibitor selectivity","volume":"29","author":"Davis","year":"2011","journal-title":"Nature Biotechnol."},{"issue":"3","key":"10.1016\/j.bspc.2026.110899_b40","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1021\/ci400709d","article-title":"Making sense of large-scale kinase inhibitor bioactivity data sets: A comparative and integrative analysis","volume":"54","author":"Tang","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.bspc.2026.110899_b41","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional block attention module","author":"Woo","year":"2018","journal-title":"Comput. Vis. \u2013 ECCV 2018"},{"issue":"3","key":"10.1016\/j.bspc.2026.110899_b42","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/0304-3800(89)90035-5","article-title":"Mean squared error of prediction as a criterion for evaluating and comparing system models","volume":"44","author":"Wallach","year":"1989","journal-title":"Ecol. Model."},{"issue":"4","key":"10.1016\/j.bspc.2026.110899_b43","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1093\/biomet\/92.4.965","article-title":"Concordance probability and discriminatory power in proportional hazards regression","volume":"92","author":"G\u00f6nen","year":"2005","journal-title":"Biometrika"},{"key":"10.1016\/j.bspc.2026.110899_b44","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1002\/jcc.23231","article-title":"Some case studies on application of \u201crm2\u201d metrics for judging quality of quantitative structure\u2013activity relationship predictions: Emphasis on scaling of response data","volume":"34","author":"Roy","year":"2013","journal-title":"J. Comput. Chem."},{"key":"10.1016\/j.bspc.2026.110899_b45","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"issue":"14","key":"10.1016\/j.bspc.2026.110899_b46","doi-asserted-by":"crossref","first-page":"5061","DOI":"10.1021\/jm100112j","article-title":"A medicinal chemist\u2019s guide to molecular interactions","volume":"53","author":"Bissantz","year":"2010","journal-title":"J. Med. Chem."},{"issue":"Database issue","key":"10.1016\/j.bspc.2026.110899_b47","first-page":"D198","article-title":"Bindingdb: a web-accessible database of experimentally determined protein\u2013ligand binding affinities","volume":"35","author":"Tiqing","year":"2007","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"10.1016\/j.bspc.2026.110899_b48","doi-asserted-by":"crossref","DOI":"10.1186\/s12864-024-10326-x","article-title":"DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks","volume":"25","author":"Kalemati","year":"2024","journal-title":"BMC Genomics"},{"issue":"Dec. Pt.B","key":"10.1016\/j.bspc.2026.110899_b49","first-page":"124647.1","article-title":"Drug-target binding affinity prediction model based on multi-scale diffusion and interactive learning","volume":"255","author":"Zhu","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110899_b50","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility","volume":"30","author":"Morris","year":"2009"},{"key":"10.1016\/j.bspc.2026.110899_b51","series-title":"Pymol","author":"Schr\u00f6dinger","year":"2020"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426014539?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426014539?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T11:38:36Z","timestamp":1785152316000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426014539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":51,"alternative-id":["S1746809426014539"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110899","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"VBMA-DTA: A multimodal drug\u2013target binding affinity prediction framework via virtual bridging and multi-attention mechanism","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110899","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110899"}}