In-silico design of potential mycobacterial membrane protein large 3 (MmpL3) inhibitors via 2D-QSAR, molecular docking, drug-likeness evaluation, and molecular dynamic simulation

Abstract

The emergence of resistant strains of Mycobacterium tuberculosis (Mtb) poses a significant challenge to tuberculosis treatment, highlighting the need for novel anti-TB agents. This study aims to design and evaluate potential MmpL3 inhibitors as promising Mtb targets using an in silico guided bioinformatics approach. We employed a combination of 2D-QSAR and molecular docking virtual screening strategies to identify potential MmpL3 inhibitors. The resulting compounds were evaluated for their pharmacokinetic profiles, ADMET properties, and binding interactions with the target protein using molecular dynamics simulations. Our study yielded a robust QSAR model with high predictive accuracy (R² = 0.931 and 0.911 for internal and external validation, respectively). Two promising compounds, 40d and 40 g, demonstrated favorable ADMET properties and stable binding interactions with the target protein, with binding free energies of −31.93 kcal/mol and −13.13 kcal/mol, respectively. The findings suggest that molecules 40d and 40 g have potential as therapeutic agents against Mtb, providing a foundation for further development and optimization of novel MmpL3 inhibitors. This study highlights the utility of in silico approaches in identifying promising lead compounds for TB treatment.