{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T01:10:09Z","timestamp":1761268209955,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Municipal Government of Quzhou","award":["2023D013","2024D003"],"award-info":[{"award-number":["2023D013","2024D003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The forecasting of Drug-Drug Interactions (DDIs) is essential in pharmacology and clinical practice to prevent adverse drug reactions. Existing approaches, often based on neural networks and knowledge graph embedding, face limitations in modeling correlations among drug features and in handling complex BioKG relations, such as one-to-many, hierarchical, and composite interactions. To address these issues, we propose Rot4Cap, a novel framework that embeds drug entity pairs and BioKG relationships into a four-dimensional vector space, enabling effective modeling of diverse mapping properties and hierarchical structures. In addition, our method integrates molecular structures and drug descriptions with BioKG entities, and it employs capsule network\u2013based attention routing to capture feature correlations. Experiments on three benchmark BioKG datasets demonstrate that Rot4Cap outperforms state-of-the-art baselines, highlighting its effectiveness and robustness.<\/jats:p>","DOI":"10.3390\/sym17111793","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:47:36Z","timestamp":1761266856000},"page":"1793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Biomedical Knowledge Graph Embedding with Hierarchical Capsule Network and Rotational Symmetry for Drug-Drug Interaction Prediction"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0449-4699","authenticated-orcid":false,"given":"Sensen","family":"Zhang","sequence":"first","affiliation":[{"name":"China Jiliang University College of Modern Science and Technology, 8 Daxue Road, Yiwu 322000, China"},{"name":"School of Information Technology, Renmin University of China, Haidian District, Beijing 100872, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Li","sequence":"additional","affiliation":[{"name":"Yangtze River Delta Research Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Yangtze River Delta Research Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China"},{"name":"Institute of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611700, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Bi","sequence":"additional","affiliation":[{"name":"Yangtze River Delta Research Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiangui","family":"Hu","sequence":"additional","affiliation":[{"name":"Yangtze River Delta Research Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"ref_1","unstructured":"Finkel, R., Clark, M.A., and Cubeddu, L.X. 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