Cheminformatics in advancing dengue antiviral research: From conventional molecular modeling (MM) to current artificial intelligence (AI) approaches

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 ,&nbsp;Sk Abdul Amin ,&nbsp;Lucia Sessa ,&nbsp;Simona Concilio ,&nbsp;Stefano Piotto ,&nbsp;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.

Abstract Image

化学信息学在推进登革热抗病毒研究中的作用:从传统的分子建模(MM)到当前的人工智能(AI)方法
化学信息学迅速发展,并引起了广泛的关注,因为它有可能加快过程,降低药物设计和开发的成本。这些技术在针对登革热病毒(DENV)的药物设计中发挥着至关重要的作用。登革热病毒是一种被忽视的热带病,仍然是全球重大的卫生负担,每年报告有数百万病例。化学信息学和人工智能(AI)驱动方法的最新进展为设计针对关键病毒蛋白的抑制剂提供了有前途的策略。本研究探索了各种化学信息学方法的应用,包括传统的分子建模(药效团定位、分子对接、分子动力学(MD)模拟、虚拟筛选),以及基于人工智能(AI)/机器学习(ML)的策略,以识别对关键DENV蛋白靶点具有高亲和力和特异性的化合物。此外,它强调了实验验证和计算机预测之间的协同作用,以优先考虑候选分子以进一步开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.50
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信