Computational approaches for identifying quorum sensing inhibitors

Quorum sensing (QS) is a critical cell-to-cell communication mechanism employed by microorganisms to coordinate their collective behaviors, including biofilm formation, virulence regulation, and development of antimicrobial resistance (AMR). Disruption of QS using quorum sensing inhibitors (QSIs) has emerged as a promising anti-virulence strategy for combating multidrug-resistant bacterial infections. This review focuses on new technologies in drug discovery applied to the identification and optimization of QSIs, with particular emphasis on computational and data-driven methods. The key methodologies discussed include molecular docking, molecular dynamics simulations, virtual screening, and quantitative structure-activity relationship (QSAR) modeling. Studies demonstrating the successful application of these technologies against clinically relevant pathogens, such as Pseudomonas aeruginosa and Chromobacterium violaceum, are highlighted to illustrate the structure-guided QSI discovery. In addition, this review examines the current challenges in QSI development, including target specificity, resistance avoidance, and translational limitations. Recent advances in machine learning, artificial intelligence assisted screening, and systems biology-based modeling are also discussed as transformative tools that enhance hit identification, lead optimization, and mechanistic understanding. Collectively, these emerging technologies underscore the potential of QS-targeted drug discovery to deliver innovative anti-virulence therapies and contribute to global efforts to mitigate AMR.