Pharmacophore, QSAR, molecular docking, molecular dynamics and ADMET study of trisubstituted benzimidazole derivatives as potent anti-tubercular agents

Abstract

The objective of this study was to develop and verify a QSAR model for predicting the antitubercular effects of trisubstituted benzimidazole derivatives. For this study, 50 compounds with antitubercular activity targeting FtsZ protein were retrieved from published studies. The PharmaGist web server was used to create the pharmacophore model. The best pharmacophore model had seven special features and a score of 56.79. Molecular descriptors were computed using PaDEL-descriptor software and the ChemDes web server. A predictive QSAR model was created using the QSARINS software. The best QSAR Model 4, which had R2: 0.9114, R2adj: 0.8924, LOF: 0.1131, Q2-F1: 0.6499, Q2-F2: 0. 6499, Q2-F3:0.8356; and CCCext: 0.8522 identified a good fit. The descriptors AATS8e, AATSC7p, SpMin2_Bhm, nHBint6, SHssNH, and XLogP were essential for antitubercular activity. Molecular descriptors, such as SpMin2_Bhm, nHBint6, SHssNH, and XLogP positively contributed to the antitubercular activity. Molecular descriptors such as AATS8e and AATSC7p were negatively associated with antitubercular activity. From the above data, we have designed four series of trisubstituted benzimidazole derivatives, including 1A–E, 2A–E, 3A–E and 4A–E. All these newly designed compounds were used for molecular docking simulation and ADMET studies. Compound 1B exhibited the best ADMET features and highest binding affinity. Consequently, it was subjected to dynamic simulations for 100 ns. The binding stability of the FtsZ protein-compound 1B complex was confirmed by RMSD, RMSF, H-bond interaction, and percentage occupancy.