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

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. • A comprehensive review of the multifaceted roles of different targets of Dengue virus (DENV). • It explores the applications of various cheminformatics methods to identify lead compounds with high affinity and specificity for critical DENV protein targets. • It highlights the synergy between experimental validation and in-silico predictions to prioritize candidate molecules. • Illustration of innovative strategies to design novel inhibitors.