Anti-tubercular drug development: computational strategies to identify potential compounds

InhA is an attractive target to combat tuberculosis (TB), which is targeted by many pro-drugs (isoniazid, etc.) and drugs such as triclosan. However, triclosan is less useful as an antitubercular drug due to its low bioavailability and therefore, in order to overcome this difficulty, many derivatives of triclosan were prepared. Here, we have combined various computational techniques to virtually screen out four potential triclosan derivatives. Molecular docking methods have been employed to screen out 32 out of 62 triclosan derivatives considering the mode of binding and the top re-rank scores. A comparative study on the chemical properties of triclosan and some of its derivatives has been performed using density functional theory (DFT) calculations. DFT based global reactivity descriptors (GRD), such as hardness, chemical potential, chemical softness, electrophilicity index, Fukui function, and local philicity calculated at the optimized geometries were used to investigate the usefulness of these descriptors for understanding the reactive nature and sites of the molecules. QSAR equations were built using these descriptors considering these 32 compounds. Four common compounds showing the best correlation and the best docking scores were considered for the ADMET property calculations and their dynamical movements have been studied using molecular dynamics simulations. Our results showed that these four compounds are chemically more active than triclosan and have the potential to inhibit the Mycobacterium tuberculosis enoyl acyl carrier protein reductase. This work shows that combination of different computational techniques may help to screen out potential drug candidates from a list of possible ones.