Exploring the Anti-Diabetic Potential of Indonesian Traditional Herbal Plants with Network Pharmacology Approach

Exploring the Anti-Diabetic Potential of Indonesian Traditional Herbal Plants with Network Pharmacology Approach | 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 Exploring the Anti-Diabetic Potential of Indonesian Traditional Herbal Plants with Network Pharmacology Approach Hito Kawiswara, Angga Aditya Permana, Analekta Tiara Perdana, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8802748/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Diabetes Mellitus (DM) is a chronic metabolic disease with increasing prevalence, and it remains a major health problem worldwide. This study investigated the anti-diabetic potential of Indonesian traditional herbal plants ( Curcuma amada Roxb., Curcuma longa , and Allium cepa L. var. aggregatum ) using a network pharmacology approach combined with molecular docking. Active compounds were collected from IJAH Analytics and KNApSAcK, screened using oral bioavailability and drug-likeness criteria, and then mapped to predicted protein targets using SwissTargetPrediction. Diabetes-related targets were obtained from OMIM, UniProt, MalaCards, and GeneCards, and overlapping targets were selected for network analysis. A protein--protein interaction network was built using STRING v12.0, followed by centrality analysis (degree, betweenness, closeness, and eigenvector). The Skyline query algorithm was applied to prioritize key proteins, and the top five proteins were selected as receptors for docking. Molecular docking was performed using AutoDock Vina against nine candidate ligands. The docking results showed that Tropeoside B1 consistently produced the strongest and most frequent binding across the selected receptors, followed by Progesterone and Peonidin-3-Arabinoside. Overall, this workflow helps narrow down promising herbal compounds and protein targets, providing candidates for further experimental validation. Diabetes Mellitus Indonesian Herbal Plants Molecular Docking Network Pharmacology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 05 Feb, 2026 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. 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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-8802748","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589172680,"identity":"30971565-c598-4493-be27-6b2f60b93963","order_by":0,"name":"Hito Kawiswara","email":"","orcid":"","institution":"Multimedia Nusantara University","correspondingAuthor":false,"prefix":"","firstName":"Hito","middleName":"","lastName":"Kawiswara","suffix":""},{"id":589172681,"identity":"224e4a3d-0021-47d6-9c2b-e984be0562a1","order_by":1,"name":"Angga Aditya 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This study investigated the anti-diabetic potential of Indonesian traditional herbal plants (\u003cem\u003eCurcuma amada\u003c/em\u003e Roxb., \u003cem\u003eCurcuma longa\u003c/em\u003e, and \u003cem\u003eAllium cepa\u003c/em\u003e L. var. \u003cem\u003eaggregatum\u003c/em\u003e) using a network pharmacology approach combined with molecular docking. Active compounds were collected from IJAH Analytics and KNApSAcK, screened using oral bioavailability and drug-likeness criteria, and then mapped to predicted protein targets using SwissTargetPrediction. Diabetes-related targets were obtained from OMIM, UniProt, MalaCards, and GeneCards, and overlapping targets were selected for network analysis. A protein--protein interaction network was built using STRING v12.0, followed by centrality analysis (degree, betweenness, closeness, and eigenvector). The Skyline query algorithm was applied to prioritize key proteins, and the top five proteins were selected as receptors for docking. 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Overall, this workflow helps narrow down promising herbal compounds and protein targets, providing candidates for further experimental validation.\u003c/p\u003e","manuscriptTitle":"Exploring the Anti-Diabetic Potential of Indonesian Traditional Herbal Plants with Network Pharmacology Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 11:39:29","doi":"10.21203/rs.3.rs-8802748/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-10T15:50:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T10:18:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T10:12:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Network Science","date":"2026-02-06T04:58:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-network-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apns","sideBox":"Learn more about [Applied Network Science](http://appliednetsci.springeropen.com/)","snPcode":"41109","submissionUrl":"https://submission.nature.com/new-submission/41109/3","title":"Applied Network Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7d1a1c7-4a45-4f25-84ac-6fd149ae9a0b","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T11:39:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 11:39:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8802748","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8802748","identity":"rs-8802748","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}