{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T12:32:23Z","timestamp":1793363543689,"version":"4.5.0"},"reference-count":35,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Biology"],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>Drug\u2013drug interaction (DDI) prediction remains a significant challenge due to the complexity of biological systems and the growing demand for precise predictions. Recent advances in deep learning have been successfully applied to DDI prediction. However, the asymmetrical nature of DDIs is always neglected, which can lead to some information loss during the feature learning process. To address the issue, a novel DDI prediction method based on knowledge graph and generative adversarial network, KGGAN-DDI, is proposed to predict potential DDIs. First, a knowledge graph embedding module is designed to capture and encode asymmetric associations between drug pairs, which can enhance the feature representation and contextual relevance of drug interactions. Then, a dual-generator GAN is adopted to produce realistic samples and improve the prediction accuracy further. Moreover, a least squares loss function is utilized to mitigate the vanishing gradient problem, thereby providing smoother gradients and enhancing the efficiency and stability of the optimization process. Extensive experiments demonstrate that the proposed KGGAN-DDI performs better than state-of-the-art methods. Case study further shows the effectiveness of KGGAN-DDI.<\/jats:p>","DOI":"10.1177\/15578666261443345","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T10:14:57Z","timestamp":1779963297000},"page":"717-732","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Asymmetric Drug\u2013Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph"],"prefix":"10.1177","volume":"33","author":[{"given":"Chaokun","family":"Yan","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6073-5003","authenticated-orcid":false,"given":"Quanao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junwei","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianlin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"Bordes A Usunier N Garcia-Duran A et al. Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems 2013;26."},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3284-5"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab169"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002512"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/10255842.2024.2311315"},{"key":"e_1_3_3_7_1","unstructured":"DrugBank. Darbepoetinalfa: Uses interactions mechanismofaction. 2025a. Available from: https:\/\/go.drugbank.com\/drugs\/DB00012"},{"key":"e_1_3_3_8_1","unstructured":"DrugBank. Dinutuximab: Uses interactions mechanism of action. 2025b. Available from: https:\/\/go.drugbank.com\/drugs\/DB09077"},{"key":"e_1_3_3_9_1","unstructured":"DrugBank. Sar9 met(o2)11substancep: Uses interactions mechanismofaction. 2025c. Available from: https:\/\/go.drugbank.com\/drugs\/DB05875"},{"key":"e_1_3_3_10_1","unstructured":"Drugs.com. Efalizumab interactions checker. 2026. Available from: https:\/\/www.drugs.com\/drug-interactions\/efalizumab.html [Last accessed: November20 2025]."},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-020-03724-x"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac151"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae143"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad215"},{"key":"e_1_3_3_15_1","article-title":"Drkg-drug repurposing knowledge graph for covid-19","author":"Ioannidis VN","year":"2020","unstructured":"Ioannidis VN, , Song X, , Manchanda S, et al. Drkg-drug repurposing knowledge graph for covid-19. arXiv Preprint 2020; arXiv: 2010.09600.","journal-title":"arXiv Preprint"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-46150-8_24"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3013-0"},{"key":"e_1_3_3_18_1","article-title":"Deepergcn: All you need to train deeper GCNS","author":"Li G","year":"2020","unstructured":"Li G, , Xiong C, , Thabet A, et al. Deepergcn: All you need to train deeper GCNS. arXiv Preprint 2020; arXiv:2006.07739.","journal-title":"arXiv Preprint"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv080"},{"key":"e_1_3_3_20_1","doi-asserted-by":"crossref","unstructured":"Lin X Quan Z Wang ZJ et al. Kgnn: Knowledge graph neural network for drug-drug interaction prediction. In: IJCAI\u201920: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. IJCAI; 2020; pp. 2739\u20132745.","DOI":"10.24963\/ijcai.2020\/380"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-5209-5_5"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz176"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.21275\/ART20203995"},{"key":"e_1_3_3_24_1","doi-asserted-by":"crossref","unstructured":"Mao X Li Q Xie H et al. Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision. IEEE; 2017; pp. 2794\u20132802.","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.1434"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0010-7824(80)80018-7"},{"issue":"8","key":"e_1_3_3_27_1","first-page":"1857","article-title":"Optimum time sequence for the administration of vincristine and cyclophosphamide in vivo","volume":"34","author":"Razek A","year":"1974","unstructured":"Razek A, , Vietti T, , Valeriote F. Optimum time sequence for the administration of vincristine and cyclophosphamide in vivo. Cancer Res 1974;34(8):1857\u20131861.","journal-title":"Cancer Res"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1803294115"},{"key":"e_1_3_3_29_1","doi-asserted-by":"crossref","unstructured":"Salha G Limnios S Hennequin R et al. Gravity-inspired graph autoencoders for directed link prediction. In: Proceedings of the 28th ACM international conference on information and knowledge management. ACM; 2019; pp. 589\u2013598.","DOI":"10.1145\/3357384.3358023"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.3945\/an.111.001800"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/jcpt.12786"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab511"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1359-6446(05)03632-9"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.09.008"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2024.109943"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10878"}],"container-title":["Journal of Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666261443345","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15578666261443345","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666261443345","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T11:47:10Z","timestamp":1793360830000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15578666261443345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":35,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7,1]]}},"alternative-id":["10.1177\/15578666261443345"],"URL":"https:\/\/doi.org\/10.1177\/15578666261443345","relation":{},"ISSN":["1066-5277","1557-8666"],"issn-type":[{"value":"1066-5277","type":"print"},{"value":"1557-8666","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]}}}