{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T17:06:30Z","timestamp":1784739990929,"version":"3.55.0"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372064"],"award-info":[{"award-number":["62372064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402528"],"award-info":[{"award-number":["62402528"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2025JJ50347"],"award-info":[{"award-number":["2025JJ50347"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2023JJ40768"],"award-info":[{"award-number":["2023JJ40768"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neucom.2026.134472","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T23:05:42Z","timestamp":1783983942000},"page":"134472","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Dynamic dual hypergraph convolutional neural networks for fine-grained drug-drug interaction prediction"],"prefix":"10.1016","volume":"700","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-0705","authenticated-orcid":false,"given":"Xiaoyong","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1482-635X","authenticated-orcid":false,"given":"Xingyu","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2989-0679","authenticated-orcid":false,"given":"Hao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4945-9974","authenticated-orcid":false,"given":"Tan","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4805-090X","authenticated-orcid":false,"given":"Ronghui","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8173-2881","authenticated-orcid":false,"given":"Mingfeng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2115-1540","authenticated-orcid":false,"given":"Wenzheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.134472_bib0005","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2021.814858","article-title":"A review of approaches for predicting drug\u2013drug interactions based on machine learning","volume":"12","author":"Han","year":"2022","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.neucom.2026.134472_bib0010","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s12918-018-0532-7","article-title":"Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization","volume":"12","author":"Yu","year":"2018","journal-title":"BMC Syst. Biol."},{"key":"10.1016\/j.neucom.2026.134472_bib0015","series-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing","first-page":"14855","article-title":"Learning to describe for predicting zero-shot drug-drug interactions","author":"Zhu","year":"2023"},{"issue":"D1","key":"10.1016\/j.neucom.2026.134472_bib0020","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkad976","article-title":"DrugBank 6.0: the DrugBank knowledgebase for 2024","volume":"52","author":"Knox","year":"2024","journal-title":"Nucleic Acids Res."},{"issue":"125","key":"10.1016\/j.neucom.2026.134472_bib0025","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.3003377","article-title":"Data-driven prediction of drug effects and interactions","volume":"4","author":"Tatonetti","year":"2012","journal-title":"Sci. Transl. Med."},{"issue":"4","key":"10.1016\/j.neucom.2026.134472_bib0030","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1109\/TCBB.2018.2858756","article-title":"Developing a multi-dose computational model for drug-induced hepatotoxicity prediction based on toxicogenomics data","volume":"16","author":"Su","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"2","key":"10.1016\/j.neucom.2026.134472_bib0035","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/s11814-023-1377-3","article-title":"Recent development of machine learning models for the prediction of drug-drug interactions","volume":"40","author":"Hong","year":"2023","journal-title":"Korean J. Chem. Eng."},{"issue":"6","key":"10.1016\/j.neucom.2026.134472_bib0040","doi-asserted-by":"crossref","DOI":"10.2196\/28277","article-title":"Drug-drug interaction predictions via knowledge graph and text embedding: instrument validation study","volume":"9","author":"Wang","year":"2021","journal-title":"JMIR Med. Inform."},{"issue":"1","key":"10.1016\/j.neucom.2026.134472_bib0045","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btac754","article-title":"Integrating heterogeneous knowledge graphs into drug\u2013drug interaction extraction from the literature","volume":"39","author":"Asada","year":"2023","journal-title":"Bioinformatics"},{"key":"10.1016\/j.neucom.2026.134472_bib0050","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"3558","article-title":"Hypergraph neural networks","author":"Feng","year":"2019"},{"key":"10.1016\/j.neucom.2026.134472_bib0055","series-title":"Proceedings of the IEEE 39th International Conference on Data Engineering","first-page":"1503","article-title":"HyGNN: drug-drug interaction prediction via hypergraph neural network","author":"Saifuddin","year":"2023"},{"key":"10.1016\/j.neucom.2026.134472_bib0060","series-title":"Proceedings of the IEEE 38th International Conference on Data Engineering","first-page":"1621","article-title":"Dynamic hypergraph convolutional network","author":"Yin","year":"2022"},{"key":"10.1016\/j.neucom.2026.134472_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126992","article-title":"Dynamic hypergraph convolutional network for multimodal sentiment analysis","volume":"565","author":"Huang","year":"2024","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.neucom.2026.134472_bib0070","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/3756102","article-title":"Hyper-Mol: molecular representation learning via fingerprint-based hypergraph","volume":"2023","author":"Cui","year":"2023","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.neucom.2026.134472_bib0075","article-title":"Improved prediction of drug-drug interactions using ensemble deep neural networks","volume":"17","author":"Vo","year":"2023","journal-title":"Med. Drug Discov."},{"key":"10.1016\/j.neucom.2026.134472_bib0080","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13721-019-0215-3","article-title":"ISCMF: integrated similarity-constrained matrix factorization for drug\u2013drug interaction prediction","volume":"9","author":"Rohani","year":"2020","journal-title":"Netw. Model. Anal. Health Inform. Bioinform."},{"key":"10.1016\/j.neucom.2026.134472_bib0085","series-title":"Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine","first-page":"1708","article-title":"CNN-DDI: a novel deep learning method for predicting drug-drug interactions","author":"Zhang","year":"2020"},{"issue":"9","key":"10.1016\/j.neucom.2026.134472_bib0090","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1109\/TKDE.2015.2415497","article-title":"Visual classification by \u21131-hypergraph modeling","volume":"27","author":"Wang","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"10.1016\/j.neucom.2026.134472_bib0095","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/TIP.2016.2621671","article-title":"Elastic net hypergraph learning for image clustering and semi-supervised classification","volume":"26","author":"Liu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.neucom.2026.134472_bib0100","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.ins.2019.03.012","article-title":"Robust \u21132-hypergraph and its applications","volume":"501","author":"Jin","year":"2019","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2026.134472_bib0105","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"409","article-title":"Learning hypergraph-regularized attribute predictors","author":"Huang","year":"2015"},{"issue":"5","key":"10.1016\/j.neucom.2026.134472_bib0110","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1000385","article-title":"Hypergraphs and cellular networks","volume":"5","author":"Klamt","year":"2009","journal-title":"PLOS Comput. Biol."},{"key":"10.1016\/j.neucom.2026.134472_bib0115","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1738","article-title":"Video object segmentation by hypergraph cut","author":"Huang","year":"2009"},{"issue":"9","key":"10.1016\/j.neucom.2026.134472_bib0120","doi-asserted-by":"crossref","first-page":"4290","DOI":"10.1109\/TIP.2012.2199502","article-title":"3-D object retrieval and recognition with hypergraph analysis","volume":"21","author":"Gao","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.neucom.2026.134472_bib0125","series-title":"HICSS 2019 Symposium on Cybersecurity Big Data Analytics","article-title":"High performance hypergraph analytics of domain name system relationships","volume":"vol. 3","author":"Joslyn","year":"2019"},{"issue":"3","key":"10.1016\/j.neucom.2026.134472_bib0130","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/TMM.2014.2298216","article-title":"Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning","volume":"16","author":"Fang","year":"2014","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.neucom.2026.134472_bib0135","series-title":"Proceedings of the 7th International Workshop on Machine Learning in Medical Imaging","first-page":"1","article-title":"Identifying high order brain connectome biomarkers via learning on hypergraph","author":"Zu","year":"2016"},{"issue":"10","key":"10.1016\/j.neucom.2026.134472_bib0140","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007384","article-title":"Hypergraph-based connectivity measures for signaling pathway topologies","volume":"15","author":"Franzese","year":"2019","journal-title":"PLOS Comput. Biol."},{"key":"10.1016\/j.neucom.2026.134472_bib0145","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ins.2022.10.006","article-title":"Dynamic hypergraph neural networks based on key hyperedges","volume":"616","author":"Kang","year":"2022","journal-title":"Inf. Sci."},{"issue":"2","key":"10.1016\/j.neucom.2026.134472_bib0150","doi-asserted-by":"crossref","first-page":"383","DOI":"10.26599\/BDMA.2024.9020091","article-title":"Census and analysis of higher-order interactions in real-world hypergraphs","volume":"8","author":"Meng","year":"2025","journal-title":"Big Data Min. Anal."},{"key":"10.1016\/j.neucom.2026.134472_bib0155","series-title":"In Silico Medicinal Chemistry: Computational Methods to Support Drug Design","first-page":"199","article-title":"Appendix D: Rdkit","author":"Brown","year":"2015"},{"issue":"1","key":"10.1016\/j.neucom.2026.134472_bib0160","doi-asserted-by":"crossref","first-page":"214","DOI":"10.26599\/BDMA.2024.9020055","article-title":"Robust non-negative matrix tri-factorization with dual hyper-graph regularization","volume":"8","author":"Yu","year":"2024","journal-title":"Big Data Min. Anal."},{"key":"10.1016\/j.neucom.2026.134472_bib0165","article-title":"Auto-encoding variational Bayes","volume":"30","author":"Kingma","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134472_bib0170","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134472_bib0175","series-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"701","article-title":"DeepWalk: online learning of social representations","author":"Perozzi","year":"2014"},{"key":"10.1016\/j.neucom.2026.134472_bib0180","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"855","article-title":"Node2Vec: scalable feature learning for networks","author":"Grover","year":"2016"},{"key":"10.1016\/j.neucom.2026.134472_bib0185","series-title":"International Conference on Learning Representations","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.neucom.2026.134472_bib0190","series-title":"International Conference on Learning Representations","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"10.1016\/j.neucom.2026.134472_bib0195","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"6","key":"10.1016\/j.neucom.2026.134472_bib0200","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab133","article-title":"SSI-DDI: substructure\u2013substructure interactions for drug\u2013drug interaction prediction","volume":"22","author":"Nyamabo","year":"2021","journal-title":"Brief. Bioinform."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226018709?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226018709?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:49:47Z","timestamp":1784738987000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226018709"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":40,"alternative-id":["S0925231226018709"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134472","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dynamic dual hypergraph convolutional neural networks for fine-grained drug-drug interaction prediction","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134472","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"134472"}}