Leveraging In-silico Methods for Laryngeal Cancer Drug Discovery Utilizing QSAR, ligand-base Design, Molecular Docking, and Pharmacokinetic Profiling Approaches.

Leveraging In-silico Methods for Laryngeal Cancer Drug Discovery Utilizing QSAR, ligand-base Design, Molecular Docking, and Pharmacokinetic Profiling Approaches. | 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 Leveraging In-silico Methods for Laryngeal Cancer Drug Discovery Utilizing QSAR, ligand-base Design, Molecular Docking, and Pharmacokinetic Profiling Approaches. Sani Abbas Muhammad Abbas, Muhammad Tukur Ibrahim Ibrahim, Adamu Uzairu Adamu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6185117/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study investigated the design and evaluation of new and more active anti-cancer compounds targeting laryngeal carcinoma Methods: In this study, quantitative structure-activity relationship modeling, ligand-based drug design, molecular docking, and pharmacokinetic studies were utilized in carrying out this research. Result and conclusion : A robust QSAR model was developed, achieving R 2 adj of 0.8257, R 2 of 0.8872 and R² pred of 0.6997, which indicated a reliable predictive capability where the model parameters EE_Dzm and SpAD_DzZ were used in designing five new compounds with compound 3C identified as the most promising candidate, exhibiting a Moldock score of -98.973kcalmol −1 , re-rank score of -69.093 kcalmol −1 , predicted activity of 5.349 and a total energy of atoms measured at -64.4248 kcalmol −1 , indicating strong binding affinity better than the template 10l and the standard drug. Most importantly, all the five designed compounds adhered to Lipinski's Rule of Five and passed drug-likeness tests, indicating favorable pharmacokinetic profiles. QSAR Laryngeal Cancer Molecular docking pharmacokinetics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6185117","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":426555018,"identity":"d5ca127d-6a98-48db-b5cb-ae42aa22f3e0","order_by":0,"name":"Sani Abbas Muhammad 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