Structural exploration of thiazole urea-based Gyr B inhibitors through different computational approaches: Bayesian classification, molecular docking and free energy-inspired molecular dynamics simulation.

The emergence of multidrug-resistant (MDR) tuberculosis (TB) has posed a major challenge to global health, primarily due to resistance against first-generation fluoroquinolones targeting DNA Gyrase subunit A (Gyr A). In this context, DNA Gyrase subunit B (Gyr B), essential for bacterial DNA replication and topological regulation, has emerged as a potential alternative drug target. Despite efforts by medicinal chemists, no FDA-approved Gyr B inhibitors are currently available for TB treatment. This study adopts a comprehensive computational approach to identify key structural features responsible for Gyr B inhibition. A statistically robust Bayesian classification model was developed using thiazole-urea derivatives to uncover essential molecular fingerprints associated with Gyr B inhibitory activity. The developed model exhibited strong predictive performances with high ROC scores for both the training and test set. Molecular docking revealed critical interactions between selected inhibitors and the Gyr B active site, validating the relevance of identified fingerprints. Additionally, molecular dynamics (MD) simulations were conducted to evaluate the stability of protein-ligand complexes, with RMSD, RMSF, and Rg analyses further supported by principal component analysis (PCA), free energy landscape (FEL) analysis, and MMPBSA analysis. Based on identified fingerprints, five novel compounds were rationally designed and validated computationally. The top two designed compounds demonstrate good ADME profile with favourable binding and dynamic stability profiles, suggesting the potential for further biological assessment. The current molecular modeling pipeline offers crucial insights that may help researchers in the rational design of novel Gyr B inhibitors against MDR-TB.