{"paper_id":"2e671f49-7c55-421f-b052-c64a2af2d67f","body_text":"Molecular Docking Studies for the Evaluation of Cannabinoids as Multi-Target Inhibitors of Type 2 Diabetes | 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 Molecular Docking Studies for the Evaluation of Cannabinoids as Multi-Target Inhibitors of Type 2 Diabetes Guy Roussel Takuissu, Dupon Bruno Ambamba, Shabnor Iqbal, Kolowole Olofinsan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5921911/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 Type 2 Diabetes is due to dysregulation of glycemic control through multiple mechanisms. Cannabinoids from Cannabis sativa L. show promise as anti-diabetic agents, though currently their molecular mechanisms are unclear. This study carried out in silico molecular docking of cannabinoids (cannabidiol (CBD), tetrahydrocannabivarin (THCV), cannabichromene (CBC), cannabigerol (CBG), cannabinol (CBN), Δ9-tetrahydrocannabinol (THC)) against key diabetes drug targets namely: dipeptidyl peptidase-4 (DPP-4), α-glucosidase, α-amylase and invertase. Enzyme structures were obtained from the RCSB Protein Data Bank, while ligand structures were generated and optimized using ChemDraw. AutoDockTools-1.5.7 determined docking parameters, and Biovia Discovery Studio visualized interaction profiles. Normal mode analysis was performed for molecular dynamics simulations. Cannabinoids used in this study showed higher binding affinities (-5.02 to -7.85 kcal/mol) than reference drugs for the targets, with the exception of DPP-4. For DPP-4, CBN (-7.85 kcal/mol) showed best affinity followed by THC (-6.64 kcal/mol). All cannabinoids interacted with catalytic residues except cannabichromene. For α-amylase, THC (-7.14 kcal/mol) was strongest, with residues Asp197, Glu233, Asp300 involved. Against invertase, THC (-7.27 kcal/mol) had highest affinity interacting with Glu230. For α-glucosidase, THC (-7.86 kcal/mol) again dominated via key residues. Normal mode analysis revealed THC binding modulated complex dynamics differently for each enzyme. This study provides insights into cannabinoids' multi-target binding profiles against important T2D drug targets, supporting their potential as natural anti-diabetic agents requiring further research. Type 2 Diabetes Cannabinoids carbohydrate digestive enzymes DPP-4 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-5921911\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":410598548,\"identity\":\"2f1abdc2-55c5-4f96-9669-245ae6319422\",\"order_by\":0,\"name\":\"Guy Roussel 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