Drug discovery guided by maximum drug likeness

Drug discovery guided by maximum drug likeness | 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Research Article Drug discovery guided by maximum drug likeness Hao-Yu Zhu, Lu Xu, Wei Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8452302/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 To overcome the high attrition rate and limited clinical translatability in drug discovery, we introduce the concept of Maximum Drug-Likeness (MDL) and develop an applicable Fivefold MDL strategy (5F-MDL) to reshape the screening paradigm. The 5F-MDL strategy integrates an ensemble of 33 deep learning sub-models to construct a 33-dimensional property spectrum that quantifies the global phenotypic alignment of candidate molecules with clinically approved drugs along five axes: physicochemical properties, pharmacokinetics, efficacy, safety, and stability. Using drug-likeness scores derived from this 33-dimensional profile, we prioritized 15 high-potential molecules from a 16-million-molecule library. Experimental validation demonstrated that the lead compound M2 not only exhibits potent antibacterial activity, with a minimum inhibitory concentration (MIC) of 25.6 µg/mL, but also achieves binding stability superior to cefuroxime, as indicated by Molecular Mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations of -38.54 kcal/mol and a root-mean-square deviation (RMSD) of 2.8 Å. This strategy could overcome scaffold constraints and offers an efficient route for discovering lead compounds with favorable prospects against drug-resistant bacteria. Drug Discovery, Design, & Development Maximum drug likeness Deep learning Virtual screening Drug discovery 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. 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-8452302","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565679893,"identity":"82917261-4eb5-44ae-876d-6f87068f444b","order_by":0,"name":"Hao-Yu Zhu","email":"","orcid":"https://orcid.org/0009-0002-9295-7936","institution":"College of Material and Chemical Engineering, Tongren University, Tongren 554300, PR China","correspondingAuthor":false,"prefix":"","firstName":"Hao-Yu","middleName":"","lastName":"Zhu","suffix":""},{"id":565679968,"identity":"1f50c391-28a9-4f2a-86a0-4c9538f5ce99","order_by":1,"name":"Lu 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