QSPR-Based Statistical Study of Uphill Topological Indices for Anti-Tuberculosis Drugs

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Abstract Tuberculosis (TB) is still one of the most infectious and lethal diseases in the world, with mortality rates surpassing even HIV/AIDS. In response to the urgent need for more effective treatments, the pharmaceutical industry is increasingly turning to Quantitative Structure-Property Relationship (QSPR) models. These models play an important role in drug discovery and development because they allow for the design of compounds with specific biological activities. Researchers hope to use QSPR techniques to better control the spread of tuberculosis and combat emerging syndromes and genetic disorders. This study aims to develop a QSPR model for tuberculosis medications utilizing six physicochemical properties through Uphill degree-based topological indices. Our research and predictive models have the potential to make a significant contribution to the development of novel tuberculosis treatments. MSC(2020): 05C10, O5C12.
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QSPR-Based Statistical Study of Uphill Topological Indices for Anti-Tuberculosis Drugs | 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 QSPR-Based Statistical Study of Uphill Topological Indices for Anti-Tuberculosis Drugs Deepa R Kamath, Vishu Kumar M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7019408/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 Tuberculosis (TB) is still one of the most infectious and lethal diseases in the world, with mortality rates surpassing even HIV/AIDS. In response to the urgent need for more effective treatments, the pharmaceutical industry is increasingly turning to Quantitative Structure-Property Relationship (QSPR) models. These models play an important role in drug discovery and development because they allow for the design of compounds with specific biological activities. Researchers hope to use QSPR techniques to better control the spread of tuberculosis and combat emerging syndromes and genetic disorders. This study aims to develop a QSPR model for tuberculosis medications utilizing six physicochemical properties through Uphill degree-based topological indices. Our research and predictive models have the potential to make a significant contribution to the development of novel tuberculosis treatments. MSC(2020): 05C10, O5C12. Applied Mathematics Molecular graph Topological indices Uphill degree Uphill topological indices Full Text Additional Declarations The authors declare no competing interests. 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. 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