Integrating Machine Learning-Based Molecular Design with Experimental Validation for the Discovery of EGFR Inhibitors in Lung Cancer

Integrating Machine Learning-Based Molecular Design with Experimental Validation for the Discovery of EGFR Inhibitors in Lung Cancer | 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 Integrating Machine Learning-Based Molecular Design with Experimental Validation for the Discovery of EGFR Inhibitors in Lung Cancer Hailing Qie, Liyuan Wang, Ce Li, Chen Wu, Yong Wang, Kuo Xiao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8984012/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract The emergence of drug resistance and off-target toxicities in epidermal growth factor receptor (EGFR) targeted therapies underscores the urgent need for novel inhibitor scaffolds. This study integrates artificial intelligence-driven generative models with experimental validation to discover novel, selective EGFR inhibitors. Utilizing REINVENT4, a reinforcement learning-based generative framework, we performed a stage-wise, multi-objective optimization using a curated dataset of active EGFR inhibitors. The optimization was guided by a composite reward function incorporating docking scores, and quantitative estimates of drug-likeness (QED). Candidate molecules were subsequently evaluated using molecular dynamics (MD) simulations, synthesized, and subjected to in vitro kinase and cellular assays. The generative pipeline successfully converged on a promising N-(quinolin-5-yl) benzenesulfonamide scaffold. Among the synthesized candidates, Hit1 (4-pyridyl derivative) exhibited potent in vitro EGFR kinase inhibition (IC50 = 21.22 nM), comparable to the approved drug Gefitinib. MD simulations analyses revealed that hydrogen bond interactions with Lys745 and proper occupation of the Val726 hydrophobic cavity are critical for binding. Notably, Hit1 demonstrated robust, targeted anti-proliferative activity against EGFR-mutant non-small cell lung cancer (NSCLC) cells (PC9 and HCC827), while displaying greater than 1000-fold selectivity over wild-type EGFR cells (A549). Our findings validate the efficacy of a target-aware reinforcement learning approach for de novo drug design. The discovered quinoline-sulfonamide derivative represents a highly promising, synthetically tractable lead compound for the development of next-generation mutation-selective EGFR inhibitors. Full Text Additional Declarations No competing interests reported. Supplementary Files preds.xlsx Supportinginformation.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Editor assigned by journal 28 Feb, 2026 Submission checks completed at journal 28 Feb, 2026 First submitted to journal 27 Feb, 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. 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-8984012","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599258428,"identity":"69323a15-99a5-4934-8af5-165cca44ab12","order_by":0,"name":"Hailing Qie","email":"","orcid":"","institution":"Affiliated Hospital of Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Hailing","middleName":"","lastName":"Qie","suffix":""},{"id":599258429,"identity":"9e265433-0481-4608-a0cd-10c92047c643","order_by":1,"name":"Liyuan Wang","email":"","orcid":"","institution":"Affiliated Hospital of Hebei 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This study integrates artificial intelligence-driven generative models with experimental validation to discover novel, selective EGFR inhibitors. Utilizing REINVENT4, a reinforcement learning-based generative framework, we performed a stage-wise, multi-objective optimization using a curated dataset of active EGFR inhibitors. The optimization was guided by a composite reward function incorporating docking scores, and quantitative estimates of drug-likeness (QED). Candidate molecules were subsequently evaluated using molecular dynamics (MD) simulations, synthesized, and subjected to \u003cem\u003ein vitro\u003c/em\u003e kinase and cellular assays. The generative pipeline successfully converged on a promising N-(quinolin-5-yl) benzenesulfonamide scaffold. Among the synthesized candidates, Hit1 (4-pyridyl derivative) exhibited potent \u003cem\u003ein vitro\u003c/em\u003e EGFR kinase inhibition (IC50\u0026thinsp;=\u0026thinsp;21.22 nM), comparable to the approved drug Gefitinib. 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