{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T08:44:43Z","timestamp":1781599483761,"version":"3.54.5"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Singapore MOE Academic Research Fund Tier 1","award":["A-8001951-00-00"],"award-info":[{"award-number":["A-8001951-00-00"]}]},{"name":"China Scholarship Council Scholarship"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12471470"],"award-info":[{"award-number":["12471470"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Fundamental Science Research Project for Mathematics and Physics","award":["22JSY039"],"award-info":[{"award-number":["22JSY039"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002412","name":"Xi\u2019an Jiaotong University","doi-asserted-by":"publisher","award":["xtr062023003"],"award-info":[{"award-number":["xtr062023003"]}],"id":[{"id":"10.13039\/501100002412","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Drug combinations can not only enhance drug efficacy but also effectively reduce toxic side effects and mitigate drug resistance. With the advancement of drug combination screening technologies, large amounts of data have been generated. The availability of large data enables researchers to develop deep learning methods for predicting drug targets for synergistic combination. However, these methods still lack sufficient accuracy for practical use, and most overlook the biological significance of their models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose the HIG-Syn (hypergraph and interaction-aware multigranularity network for drug synergy prediction) model, which integrates a coarse-granularity module and a fine-granularity module to predict drug combination synergy. The former utilizes a hypergraph to capture global features, while the latter employs interaction-aware attention to simulate biological processes by modeling substructure\u2013substructure and substructure\u2013cell line interactions. HIG-Syn outperforms state-of-the-art machine learning models on our validation datasets extracted from the DrugComb and GDSC2 databases. Furthermore, the fact that five of the 12 novel synergistic drug combinations predicted by HIG-Syn are strongly supported by experimental evidence in the literature underscores its practical potential.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code is available at https:\/\/github.com\/gracygyx\/HIGSyn<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf215","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:03:41Z","timestamp":1752584621000},"page":"i86-i95","source":"Crossref","is-referenced-by-count":1,"title":["HIG-Syn: a hypergraph and interaction-aware multigranularity network for predicting synergistic drug combinations"],"prefix":"10.1093","volume":"41","author":[{"given":"Yuexi","family":"Gu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , Xi'an, Shaanxi 710049, People's Republic of","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3633-0725","authenticated-orcid":false,"given":"Jian","family":"Zu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , Xi'an, Shaanxi 710049, People's Republic of","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongheng","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University , Xi'an, Shaanxi 710049, People's Republic of","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0260-824X","authenticated-orcid":false,"given":"Louxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Centre for Data Science and Machine 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