{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:51:03Z","timestamp":1780764663949,"version":"3.54.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T00:00:00Z","timestamp":1684281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["61872297"],"award-info":[{"award-number":["61872297"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Provincial Key Research & Development Program, China","award":["2023-YBSF-114"],"award-info":[{"award-number":["2023-YBSF-114"]}]},{"name":"CAAI-Huawei MindSpore Open Fund","award":["CAAIXSJLJJ-2022-035A"],"award-info":[{"award-number":["CAAIXSJLJJ-2022-035A"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["SY20210003"],"award-info":[{"award-number":["SY20210003"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Center for High Performance Computation"},{"DOI":"10.13039\/501100002663","name":"Northwestern Polytechnical University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug\u2013drug interactions (DDI) may lead to adverse reactions in human body and accurate prediction of DDI can mitigate the medical risk. Currently, most of computer-aided DDI prediction methods construct models based on drug-associated features or DDI network, ignoring the potential information contained in drug-related biological entities such as targets and genes. Besides, existing DDI network-based models could not make effective predictions for drugs without any known DDI records. To address the above limitations, we propose an attention-based cross domain graph neural network (ACDGNN) for DDI prediction, which considers the drug-related different entities and propagate information through cross domain operation. Different from the existing methods, ACDGNN not only considers rich information contained in drug-related biomedical entities in biological heterogeneous network, but also adopts cross-domain transformation to eliminate heterogeneity between different types of entities. ACDGNN can be used in the prediction of DDIs in both transductive and inductive setting. By conducting experiments on real-world dataset, we compare the performance of ACDGNN with several state-of-the-art methods. The experimental results show that ACDGNN can effectively predict DDIs and outperform the comparison models.<\/jats:p>","DOI":"10.1093\/bib\/bbad155","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T16:11:26Z","timestamp":1684339886000},"source":"Crossref","is-referenced-by-count":22,"title":["Attention-based cross domain graph neural network for prediction of drug\u2013drug interactions"],"prefix":"10.1093","volume":"24","author":[{"given":"Hui","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710072 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"KangKang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710072 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"WenMin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710072 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ShuangHong","family":"Song","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Shaanxi Normal University , Xi\u2019an 710119 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Gao","sequence":"additional","affiliation":[{"name":"Rocket Force University of Engineering , Xi\u2019an 710025 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JianYu","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Northwestern Polytechnical University , Xi\u2019an 710072 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"issue":"1","key":"2023072020020415000_ref1","first-page":"16","article-title":"Predicting drug\u2013drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge","volume":"9","author":"Takeda","year":"2017","journal-title":"J Chem"},{"key":"2023072020020415000_ref2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ins.2017.06.021","article-title":"Drug-drug interaction extraction from biomedical literature using support vector machine and long short term memory networks","volume":"415","author":"Huang","year":"2017","journal-title":"Inform Sci"},{"issue":"4","key":"2023072020020415000_ref3","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/TCBB.2021.3081268","article-title":"A comprehensive review of computational methods for drug-drug interaction detection","volume":"19","author":"Qiu","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023072020020415000_ref4","first-page":"3756","article-title":"CSGNN: Contrastive self-supervised graph neural network for molecular interaction prediction","volume-title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI, Virtual Event \/ Montreal, Canada, 19\u201327 August","author":"Zhao","year":"2021"},{"issue":"18","key":"2023072020020415000_ref5","doi-asserted-by":"crossref","first-page":"E4304","DOI":"10.1073\/pnas.1803294115","article-title":"Deep learning improves prediction of drug\u2013drug and drug\u2013food interactions","volume":"115","author":"Ryu","year":"2018","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2023072020020415000_ref6","first-page":"774","article-title":"Predicting drug-drug interactions through large-scale similarity-based link prediction","volume-title":"The Semantic Web. 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