Machine Learning-Driven Discovery and Experimental Validation of Novel STING Inhibitors from Traditional Chinese Medicine

Machine Learning-Driven Discovery and Experimental Validation of Novel STING Inhibitors from Traditional Chinese Medicine | 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 Machine Learning-Driven Discovery and Experimental Validation of Novel STING Inhibitors from Traditional Chinese Medicine Tian Zhao, Dan Chen, Zongjun Chen, Qionghui Wang, Jun Qing, Qiang Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9357878/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 17 You are reading this latest preprint version Abstract The stimulator of interferon genes (STING) is a key signaling adaptor in the cGAS-STING pathway of the innate immune system, playing a significant role in autoimmune diseases, viral infections, and cancer, thus representing a promising target for small-molecule inhibitor therapies. This study presents an integrated multi-dimensional computer-aided drug design (CADD) approach that utilizes machine learning (ML), molecular docking, molecular dynamics (MD) simulations, and ADMET prediction to efficiently discover new STING inhibitors from natural products. We developed a precise ML-based STING classification model with 98.5% accuracy and a robust STING inhibitor activity regression model demonstrating strong predictive capabilities, evidenced by an R² of 0.826, MAE of 0.357, and RMSE of 0.452. Virtual screening across multiple traditional Chinese medicine (TCM) compound libraries (Tao Shu L6810, TCMIO, TCMBank, and HERB) yielded 1,596 compounds with predicted pIC50 ≥ 7.00. After rigorous multi-step screening, seven compounds were selected for ADMET evaluation and experimental validation. Notably, two natural compounds, Cassiaside and Plantaginin, showed STING pathway-suppressive activity in THP-1-derived macrophages, and MD simulations further validated their stable binding to the STING protein. Collectively, this study provides a robust and accurate ML-driven strategy for STING inhibitor discovery and identifies two promising TCM-derived lead compounds, offering valuable structural scaffolds for the rational design of STING-targeted therapeutics against immune and inflammatory diseases. Stimulator of interferon genes (STING) Machine learning Molecular docking ADMET Traditional Chinese medicine compounds Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 24 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 08 Apr, 2026 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-9357878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626795595,"identity":"348b29e7-c779-4788-8e4d-7e0ce5ceceda","order_by":0,"name":"Tian Zhao","email":"","orcid":"","institution":"Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Zhao","suffix":""},{"id":626795596,"identity":"11450faf-9446-40cb-9969-b8fae563a2fa","order_by":1,"name":"Dan Chen","email":"","orcid":"","institution":"Zunyi Medical 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