GHZ-Calibrated Quantum–Geometry for Skin Repair: Entropy-Guided Pharmacophoric-ODEs with ΔSASA-Energetics for Fibrocyte-Centric Designs 

GHZ-Calibrated Quantum–Geometry for Skin Repair: Entropy-Guided Pharmacophoric-ODEs with ΔSASA-Energetics for Fibrocyte-Centric Designs | 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 Article GHZ-Calibrated Quantum–Geometry for Skin Repair: Entropy-Guided Pharmacophoric-ODEs with ΔSASA-Energetics for Fibrocyte-Centric Designs Ioannis Grigoriadis¹²³⁴ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7741909/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 Fibrocytes (CD34⁺/CD45⁺) support ECM-renewal and wound remodeling yet are rarely targeted by cosmetic-grade actives [1–3]. I present a compact discovery funnel that links (i) information-preserving library triage with ADME/drug-likeness filters, (ii) ΔSASA-aware end-point energetics layered on MM/GB(P)SA, (iii) curvature- and pose-constrained pharmacophoric ODEs, and (iv) a GHZ-calibrated hybrid quantum learner (QAOA⊗PQC). Phytochemical libraries from ZINC/DrugBank/PubChem are pared with SwissTargetPrediction/SwissADME plus QED and synthetic-accessibility to retain chemotype diversity while enforcing developability [11,22–24,29,31–33]. Binding assessment augments docking and MM/GBSA with a linear non-polar term, ΔGₙₚ=γΔSASA, restoring hydrophobic burial often underweighted by end-point methods [12–15,67–71]. Pose evolution is regularized by pharmacophore constraints in concave, hydrated pockets and integrated with SO(3)-respecting moves before MD-refinement [12–15,39,42,49–54,72]. Quantum training is gated by a digitized-adiabatic GHZ-suite: prepared GHZ-states, von Neumann entropy, and entanglement-witness tests set acceptance thresholds prior to learning [125,127,131–132,146]. The downstream QAOA⊗PQC regressor/classifier uses SPSA on Qiskit/Aer, with cross-entropy benchmarking as a hardware–software check [109–111,136,140–200]. Applied to >12,000 phytochemicals against MMP-1, neutrophil elastase, and the elastin receptor complex (EBP/NEU1/PPCA) [4–8,154,169–172], the funnel yielded ~1,200 diverse seeds, 220 redocked candidates, and 64 MD/MM-PBSA–refined leads. A botanical triad (Ginkgo biloba, Punica granatum, Morus alba) achieved in-silico potencies of 0.9–3.4 μM (MMP-1) and 1.5–5.8 μM (elastase), with Bliss/Chou–Talalay synergy CSI=1.37±0.09 [115–116]. On held-out tests, the hybrid quantum–physics model gave the lowest error (MSE≈0.52; parity R²≈0.99), surpassing ridge, calibrated physics-only, raw MM-PBSA, and docking-only baselines [29–31,70–71,101,140–200]. Chemotype-bucket analysis showed ~22%±3% lower weighted MSE versus non-hybrid comparators, and adding Stemaflex™ peptide–polyphenol cofactors further reduced error, aligning with ERC-linked biology and fibrocyte-relevant network improvements [11,31,70–71,119–121,124,154,159–160,169–172]. Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery fibrocytes ECM repair elastin receptor complex (EBP/NEU1/PPCA) MMP-1 neutrophil elastase pharmacophoric ODEs ΔSASA MM-PBSA QAOA PQC SPSA entanglement witness GHZ calibration Qiskit/Aer phytochemicals chemotype diversity Full Text Additional Declarations No competing interests reported. Supplementary Files SIAppendixIMethodsPseudocodeFlowCytometryResultsStmrt.docx 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-7741909","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586204580,"identity":"3fe5df7c-38b5-4b47-83dd-4109c7690674","order_by":0,"name":"Ioannis Grigoriadis¹²³⁴","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACxgYGhgNAmoeBIbGB4WODBIlaGGcSowUJJDAw8zYQoY65f+3Dg18q7snwtyc3f7bdYSEn79578MEPhtp8nA6b8dzgsMyZYh6JMw/bpHPPSBgbnjmXbNjDcNwSl42MM44xHJZsS+BhuJHYxpzbJpG4cUaOmQQPwzED3LZAtcjfSGz+bAnSMv+N+c8/+LT0tzEc/AjUYnAjsUGaEahlvgSPGTMPQw0eW9gYDjOcSeAxBPpFshfoFwOeHGNpGYMDOLUY9h9j/vijIsFe7nj64w8/d9TJybefMfz4pqIOt5YZwOjgQRYxOAAmD+PSwSDPf4CB8QeKSAOYqsOpZRSMglEwCkYcAACLCF263UljHAAAAABJRU5ErkJggg==","orcid":"","institution":"Biogenea Pharmaceuticals (Greece)","correspondingAuthor":true,"prefix":"","firstName":"Ioannis","middleName":"","lastName":"Grigoriadis¹²³⁴","suffix":""}],"badges":[],"createdAt":"2025-09-29 12:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7741909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7741909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103506358,"identity":"64d0e8b7-0d2d-4c5b-94ce-fed1fbebaf69","added_by":"auto","created_at":"2026-02-26 13:35:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10276149,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptStmrtGrigoriadisIoannis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7741909/v1_covered_6228e001-46da-4797-9dbc-203381408e14.pdf"},{"id":102335816,"identity":"668de771-2912-440b-8545-0b03e5e6693b","added_by":"auto","created_at":"2026-02-10 16:03:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22817002,"visible":true,"origin":"","legend":"","description":"","filename":"SIAppendixIMethodsPseudocodeFlowCytometryResultsStmrt.docx","url":"https://assets-eu.researchsquare.com/files/rs-7741909/v1/3caee7b2f29714311ce9d0ff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"GHZ-Calibrated Quantum–Geometry for Skin Repair: Entropy-Guided Pharmacophoric-ODEs with ΔSASA-Energetics for Fibrocyte-Centric Designs ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"fibrocytes, ECM repair, elastin receptor complex (EBP/NEU1/PPCA), MMP-1, neutrophil elastase, pharmacophoric ODEs, ΔSASA, MM-PBSA, QAOA, PQC, SPSA, entanglement witness, GHZ calibration, Qiskit/Aer, phytochemicals, chemotype diversity","lastPublishedDoi":"10.21203/rs.3.rs-7741909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7741909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFibrocytes (CD34⁺/CD45⁺) support ECM-renewal and wound remodeling yet are rarely targeted by cosmetic-grade actives [1–3]. I present a compact discovery funnel that links (i) information-preserving library triage with ADME/drug-likeness filters, (ii) ΔSASA-aware end-point energetics layered on MM/GB(P)SA, (iii) curvature- and pose-constrained pharmacophoric ODEs, and (iv) a GHZ-calibrated hybrid quantum learner (QAOA⊗PQC). Phytochemical libraries from ZINC/DrugBank/PubChem are pared with SwissTargetPrediction/SwissADME plus QED and synthetic-accessibility to retain chemotype diversity while enforcing developability [11,22–24,29,31–33]. Binding assessment augments docking and MM/GBSA with a linear non-polar term, ΔGₙₚ=γΔSASA, restoring hydrophobic burial often underweighted by end-point methods [12–15,67–71]. Pose evolution is regularized by pharmacophore constraints in concave, hydrated pockets and integrated with SO(3)-respecting moves before MD-refinement [12–15,39,42,49–54,72].\u003c/p\u003e\n\u003cp\u003eQuantum training is gated by a digitized-adiabatic GHZ-suite: prepared GHZ-states, von Neumann entropy, and entanglement-witness tests set acceptance thresholds prior to learning [125,127,131–132,146]. The downstream QAOA⊗PQC regressor/classifier uses SPSA on Qiskit/Aer, with cross-entropy benchmarking as a hardware–software check [109–111,136,140–200]. Applied to \u0026gt;12,000 phytochemicals against MMP-1, neutrophil elastase, and the elastin receptor complex (EBP/NEU1/PPCA) [4–8,154,169–172], the funnel yielded ~1,200 diverse seeds, 220 redocked candidates, and 64 MD/MM-PBSA–refined leads. A botanical triad (Ginkgo biloba, Punica granatum, Morus alba) achieved in-silico potencies of 0.9–3.4 μM (MMP-1) and 1.5–5.8 μM (elastase), with Bliss/Chou–Talalay synergy CSI=1.37±0.09 [115–116]. On held-out tests, the hybrid quantum–physics model gave the lowest error (MSE≈0.52; parity R²≈0.99), surpassing ridge, calibrated physics-only, raw MM-PBSA, and docking-only baselines [29–31,70–71,101,140–200]. Chemotype-bucket analysis showed ~22%±3% lower weighted MSE versus non-hybrid comparators, and adding Stemaflex™ peptide–polyphenol cofactors further reduced error, aligning with ERC-linked biology and fibrocyte-relevant network improvements [11,31,70–71,119–121,124,154,159–160,169–172].\u003c/p\u003e","manuscriptTitle":"GHZ-Calibrated Quantum–Geometry for Skin Repair: Entropy-Guided Pharmacophoric-ODEs with ΔSASA-Energetics for Fibrocyte-Centric Designs ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 16:02:47","doi":"10.21203/rs.3.rs-7741909/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ba27210-9104-4777-a459-498a6d6b9dff","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62369398,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62369399,"name":"Biological sciences/Drug discovery"}],"tags":[],"updatedAt":"2026-02-24T07:12:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 16:02:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7741909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7741909","identity":"rs-7741909","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}