ConvAHKG: Action-Based Hybrid Knowledge Graph with a Dual-Channel Convolutional Approach for Drug Repurposing

ConvAHKG: Action-Based Hybrid Knowledge Graph with a Dual-Channel Convolutional Approach for Drug Repurposing | 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 Article ConvAHKG: Action-Based Hybrid Knowledge Graph with a Dual-Channel Convolutional Approach for Drug Repurposing Marzieh Khodadadi AghGhaleh, Rooholah Abedian, Reza Zarghami, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7558566/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 17 You are reading this latest preprint version Abstract Drug repurposing, which is the process of finding new therapeutic usage for already approved medications, has become a more efficient, time-saving and cost-effective approach compared to traditional drug discovery methods. ConvAHKG, an action-based hybrid knowledge graph approach, is proposed to improve the prediction of drug-disease associations by leveraging biological relationships among drugs, proteins, and diseases. AHKG is designed to integrate both drug and disease features to provide a comprehensive framework. To represent these relationships, Word2Vec embeddings are used to capture the semantic similarities among entities, and a novel dual-channel 1D convolutional neural network (IDC_Conv1D) is introduced for the classification of drug-disease pairs. This architecture is specifically intended to handle the complexity and heterogeneity of biological data. Furthermore, to tackle the challenge of class imbalance in the dataset, a weighted binary cross-entropy loss function is proposed, which significantly improved the model's predictive performance. ConvAHKG outperforms state-of-the-art models, with an AUC of 0.9836 and an AUPRC of 0.9686. To validate its practical utility, we apply ConvAHKG to study non-small cell lung cancer (NSCLC), the most prevalent form of lung cancer. Through this application, we identify promising repurposed drugs, such as Trastuzumab, that have the potential to treat NSCLC. Additionally, in the case of a predicted compound which was not experimentally validated, molecular docking studies showed strong binding interactions, further confirming its potential as a novel therapeutic candidate. All data and code used in this study are available at { https://github.com/Marzieh-Khodadadi/ConvAHKG} Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Full Text Additional Declarations No competing interests reported. Supplementary Files submitSeptember.zip github.txt Supplementary.xlsx Cite Share Download PDF Status: Published Journal Publication published 06 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviews received at journal 28 Nov, 2025 Reviews received at journal 28 Nov, 2025 Reviews received at journal 22 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers invited by journal 17 Nov, 2025 Editor invited by journal 07 Nov, 2025 Editor assigned by journal 23 Sep, 2025 Submission checks completed at journal 17 Sep, 2025 First submitted to journal 17 Sep, 2025 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. 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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-7558566","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":549161425,"identity":"57b6f7a4-6df5-4f1f-8cbe-f8700203ef72","order_by":0,"name":"Marzieh Khodadadi AghGhaleh","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Marzieh","middleName":"Khodadadi","lastName":"AghGhaleh","suffix":""},{"id":549161426,"identity":"435a11c4-b19b-4dc8-8dff-00ed3db88fde","order_by":1,"name":"Rooholah Abedian","email":"","orcid":"","institution":"University of 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