Combining Triazole Scaffold Repurposing and Generative Transformer Architecture for Structure-Based Inhibitor Design...

Background: The rapid escalation of multidrug-resistant P. aeruginosa necessitates anti-virulence strategies targeting quorum sensing rather than bacterial survival; however, integrating scaffold repurposing with generative AI to inhibit LasR remains underexplored. Here, we address this gap by combining triazole scaffold mining with transformer-based de novo molecular generation to systematically identify putative LasR inhibitors. Methods: An integrated computational pipeline involving Structure-based inhibitor design using Generative Transformer Architecture, deep learning-assisted GNINA rescoring, density functional theory optimization, and molecular dynamics simulations was employed, followed by MM-GBSA binding free energy estimation. Results: Screening of 2666 triazole derivatives and 19,861 DrugGPT-generated compounds yielded top hits with superior binding affinities (-11.59 to -13.81 kcal/mol) compared to the reference ligand (-8.50 kcal/mol). MD simulations yielded stable protein-ligand complexes with RMSD values of 2.24-3.01 Å, while key interactions involving residues Tyr50, Asp67, and Ser123 were consistently maintained. Binding free energy calculations further confirmed strong thermodynamic stability, with MM-GBSA ΔG_bind values significantly favorable, supporting robust ligand-receptor affinity. Conclusions: Collectively, these findings establish a powerful AI-integrated framework for anti-virulence drug discovery and identify structurally diverse, high-affinity triazole-based and de novo compounds as promising lead candidates for disrupting LasR-mediated quorum sensing in P. aeruginosa.