MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response Prediction

MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MGATAF: Multi-channel Graph AttentionNetwork with Adaptive Fusion forCancer-Drug Response Prediction Dhekra Saeed, Huanlai Xing, Barakat AlBadani, Li Feng, Raeed Al-Sabri, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4688819/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2025 Read the published version in BMC Bioinformatics → Version 1 posted 4 You are reading this latest preprint version Abstract Drug response prediction is critical in personalized medicine, aiming todetermine the most effective and safe treatments for individual patients.Traditional prediction methods relying on demographic and genetic dataoften fall short in accuracy and robustness. Recent graph-based models,while promising, frequently neglect the critical role of atomic interactionsand fail to integrate drug fingerprints with SMILES for comprehensivemolecular graph construction. We introduces MGATAF (MultimodalMulti-channel Graph Attention Network and Adaptive Fusion), a frame-work designed to enhance drug response predictions by capturing bothlocal and global interactions among graph nodes. MGATAF improvesdrug representation by integrating SMILES and fingerprints, resulting inmore precise predictions of drug effects. The methodology involves con-structing multimodal molecular graphs, employing multi-channel graph attention networks to capture diverse interactions, and using adap-tive fusion to integrate these interactions at multiple abstraction levels.Empirical results demonstrate MGATAF’s superior performance com-pared to traditional and other graph-based techniques. For example,on the GDSC dataset, MGATAF achieved a 5.12% improvement in thePearson correlation coefficient (PCC), reaching 0.9312 with an RMSE of0.0225.Similary, in new cell-line tests, MGATAF outperformed baselineswith a PCC of 0.8536 and an RMSE of 0.0321 on the GDSC dataset,and a PCC of 0.7364 with an RMSE of 0.0531 on the CCLE dataset Graph Neural Network Drug Response Prediction Personalized medicine Bioinformatics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2025 Read the published version in BMC Bioinformatics → Version 1 posted Editorial decision: Revision requested 09 Jul, 2024 Editor assigned by journal 08 Jul, 2024 Submission checks completed at journal 08 Jul, 2024 First submitted to journal 04 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4688819","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324628654,"identity":"99c314c4-e0e0-4dc3-b1ee-095288e17a04","order_by":0,"name":"Dhekra Saeed","email":"","orcid":"","institution":"School of Computing and Artificial Intelligence Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Dhekra","middleName":"","lastName":"Saeed","suffix":""},{"id":324628655,"identity":"ee63af24-cebb-4a68-bc90-f6d1d100ec69","order_by":1,"name":"Huanlai 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