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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The therapeutic targeting of kinase signaling pathways represents a pivotal strategy in gastric cancer, yet the rational design of single agents capable of dual-kinase inhibition remains a challenge in precision oncology. Here, we develop the\n                    <jats:bold>DuoKinaseNet<\/jats:bold>\n                    , a dual-task spectral graph neural network that integrates global topological information from a heterogeneous biomedical graph to enable structure-preserving prediction of drug\u2013kinase interactions. The core innovation of our model is the Structure-Preserving Spectral Expansion (SPSE) module, which injects global graph topology from a biomedical knowledge graph into the learning process via spectral coordinates and diffusion-distance biased attention. Evaluated on a comprehensive dataset curated from DrugBank, DuoKinaseNet achieves state-of-the-art performance, particularly on the challenging \u201cunseen protein\u201d benchmark, with an AUC-ROC of 0.903 for HER2 and 0.895 for FGFR2b. It significantly outperforms a wide range of baseline models, including 3D-aware methods and single-task variants, empirically validating the synergistic benefits of the dual-task learning and SPSE frameworks.\n                  <\/jats:p>","DOI":"10.1038\/s41746-025-02240-7","type":"journal-article","created":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T10:52:32Z","timestamp":1766227952000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["The structure-preserving spectral graph neural network for dual kinase inhibitors and synergy scoring in gastric cancer"],"prefix":"10.1038","volume":"9","author":[{"given":"Yang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhong","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longgang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujia","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanpeng","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanlin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,20]]},"reference":[{"key":"2240_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray, F. et al. 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This work exclusively utilizes de-identtified datasets available from public repositories.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"1"}}