Rinki Prasad Bhagat , Sk Abdul Amin , Lucia Sessa , Simona Concilio , Stefano Piotto , Shovanlal Gayen
{"title":"Cheminformatics in advancing dengue antiviral research: From conventional molecular modeling (MM) to current artificial intelligence (AI) approaches","authors":"Rinki Prasad Bhagat , Sk Abdul Amin , Lucia Sessa , Simona Concilio , Stefano Piotto , Shovanlal Gayen","doi":"10.1016/j.ejmcr.2025.100295","DOIUrl":null,"url":null,"abstract":"<div><div>Cheminformatics has rapidly evolved and garnered widespread attention due to its potential to accelerate the process and reduce the cost of drug design and development. These technologies play a crucial role in drug design against dengue virus (DENV), a neglected tropical disease that remains a significant global health burden, with millions of cases reported annually. Recent advancements in cheminformatics and artificial intelligence (AI)-driven approaches offer promising strategies for designing inhibitors targeting key viral proteins. This study explores the applications of various cheminformatics methods, including conventional molecular modeling (pharmacophore mapping, molecular docking, molecular dynamics (MD) simulations, virtual screening), and artificial intelligence (AI)/machine learning (ML)-based strategies reported to identify compounds with high affinity and specificity for critical DENV protein targets. Additionally, it highlights the synergy between experimental validation, and <em>in silico</em> predictions to prioritize candidate molecules for further development.</div></div>","PeriodicalId":12015,"journal":{"name":"European Journal of Medicinal Chemistry Reports","volume":"15 ","pages":"Article 100295"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Medicinal Chemistry Reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772417425000512","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Cheminformatics has rapidly evolved and garnered widespread attention due to its potential to accelerate the process and reduce the cost of drug design and development. These technologies play a crucial role in drug design against dengue virus (DENV), a neglected tropical disease that remains a significant global health burden, with millions of cases reported annually. Recent advancements in cheminformatics and artificial intelligence (AI)-driven approaches offer promising strategies for designing inhibitors targeting key viral proteins. This study explores the applications of various cheminformatics methods, including conventional molecular modeling (pharmacophore mapping, molecular docking, molecular dynamics (MD) simulations, virtual screening), and artificial intelligence (AI)/machine learning (ML)-based strategies reported to identify compounds with high affinity and specificity for critical DENV protein targets. Additionally, it highlights the synergy between experimental validation, and in silico predictions to prioritize candidate molecules for further development.