Machine Learning Prediction of Intestinal α-Glucosidase Inhibitors Using a Diverse Set of Ligands: A Drug Repurposing Effort with DrugBank Database Screening

Machine Learning Prediction of Intestinal α-Glucosidase Inhibitors Using a Diverse Set of Ligands: A Drug Repurposing Effort with DrugBank Database Screening | 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 Machine Learning Prediction of Intestinal α-Glucosidase Inhibitors Using a Diverse Set of Ligands: A Drug Repurposing Effort with DrugBank Database Screening Adeshina I. Odugbemi, Clement Nyirenda, Alan Christoffels, Samuel A. Egieyeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4265680/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jun, 2025 Read the published version in In Silico Pharmacology → Version 1 posted 15 You are reading this latest preprint version Abstract The global rise in diabetes mellitus (DM) poses a significant health challenge, necessitating effective therapeutic interventions. α-Glucosidase inhibitors play a crucial role in managing postprandial hyperglycemia and reducing the risk of complications in Type 2 DM. Quantitative Structure-Activity Relationship (QSAR) modeling is critical in computational drug discovery. However, many QSAR studies on α-glucosidase inhibitors often rely on limited compound series and statistical methods, restricting their applicability across wide chemical space. Integrating machine learning (ML) into QSAR offers a promising avenue for discovering novel therapeutic compounds by handling complex information from diverse compound sets. Our study aimed to develop robust predictive models for α-glucosidase inhibitors using a dataset of 1082 compounds with known activity against intestinal α-glucosidase (maltase-glucoamylase). After thorough data preparation, we employed 626 compounds to train ML models, generating different training data of three distinct molecular representations: 2D-descriptors, 3D-descriptors, and Extended-connectivity-fingerprint (ECFP4). These models, trained on random forest and support vector machine algorithms, underwent rigorous evaluation using established metrics. Subsequently, the best-performing model was utilized to screen the Drugbank database, identifying potential α-glucosidase inhibitor drugs. Drug repurposing, an expedited strategy for identifying new therapeutic uses for existing drugs, holds immense potential in this regard. Molecular docking and dynamics simulations further corroborated our predictions. Our results indicate that 2D descriptors and ECFP4 molecular representations outperform 3D descriptors. Furthermore, drug candidates identified from DrugBank screening exhibited promising binding interactions with α-glucosidase, corroborating our ML predictions and supporting their potential for drug repurposing. QSAR machine learning diabetes α-glucosidase drug repurposing virtual screening Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile2.xlsx Supplementaryfile1.docx Supplementaryfile1.docx Supplementaryfile2.xlsx Cite Share Download PDF Status: Published Journal Publication published 25 Jun, 2025 Read the published version in In Silico Pharmacology → Version 1 posted Editorial decision: Revision requested 17 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers agreed at journal 04 Apr, 2025 Reviewers agreed at journal 04 Apr, 2025 Reviewers invited by journal 18 Apr, 2024 Submission checks completed at journal 15 Apr, 2024 Editor assigned by journal 15 Apr, 2024 First submitted to journal 14 Apr, 2024 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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