AI-Engineered Metal–Organic Framework Nanoparticles for Quorum Sensing Disruption in Antibiotic-Resistant Biofilms
Abstract
Antibiotic-resistant biofilms represent a major challenge because their extracellular polymeric substances (EPS) and altered physiology reduce susceptibility to standard antibiotics. Quorum sensing (QS) coordinates biofilm maturation, virulence factor secretion, and stress tolerance through autoinducer signaling. Here, we describe an artificial intelligence (AI)-guided workflow to design metal–organic framework nanoparticles (MOF-NPs) optimized for QS disruption (“quorum quenching”) and antibiofilm activity. A supervised learning model prioritized MOF features (metal node, linker chemistry, pore size, surface charge, hydrophilicity, and functional groups) that maximize QS signal suppression and biofilm inhibition. AI-recommended MOF-NPs were synthesized, functionalized with an AHL-lactonase quorum-quenching enzyme and/or QS-interfering ligands, then evaluated against multidrug-resistant Pseudomonas aeruginosa and Staphylococcus aureus biofilms. The optimized MOF-NPs showed strong QS signal reduction and substantial decreases in biofilm biomass, viable counts, and EPS components, supporting AI-guided MOF engineering as a promising antibiofilm strategy.
How to Cite
Shalini Saini, "AI-Engineered Metal–Organic Framework Nanoparticles for Quorum Sensing Disruption in Antibiotic-Resistant Biofilms", Vol. 3, Issue 1, 06-01-2025, pp. 37-53.
