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Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.<\/jats:p>","DOI":"10.1093\/bib\/bbae015","type":"journal-article","created":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T14:00:49Z","timestamp":1707573649000},"source":"Crossref","is-referenced-by-count":53,"title":["SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4498-1460","authenticated-orcid":false,"given":"Yue","family":"Guo","sequence":"first","affiliation":[{"name":"Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University , 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University , 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang , China"},{"name":"Polytechnic Institute, Zhejiang University , 269 Shixiang Road,310000, Hangzhou, Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University , 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang , China"},{"name":"Polytechnic Institute, Zhejiang University , 269 Shixiang Road,310000, Hangzhou, Zhejiang , 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University , 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2813-6404","authenticated-orcid":false,"given":"Ji","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Pharmacology and Toxicology, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University , 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang , China"},{"name":"Engineering Research Center of Innovative Anticancer Drugs, Ministry of Education , 866 Yuhangtang Road, 310058, Hangzhou, Zhejiang , China"},{"name":"Center for Medical Research and Innovation in Digestive System Tumors, Ministry of Education , 310020, Hangzhou, Zhejiang , China"},{"name":"The Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University , 291 Fucheng Road, 310018, Hangzhou, Zhejiang , China"},{"name":"Cancer Center, Zhejiang University , 866 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