QSAR modeling, molecular docking, dynamic simulation and ADMET study of novel tetrahydronaphthalene derivatives as potent antitubercular agents

Abstract

Abstract Background Tuberculosis is an air-borne contagious disease caused by slow-growing Mycobacterium tuberculosis (Mtb). According to Global Tuberculosis Report 2022, 1.6 million people were infected by tuberculosis in 2021. The continuing spread of drug-resistant tuberculosis (TB) is one of the most difficult challenges to control the tuberculosis. So new drug discovery is essential to the treatment of tuberculosis. This study aims to develop a QSAR model to predict the antitubercular activity of tetrahydronaphthalene derivatives. The QSARINS was used in this study to develop the QSAR predictive model. Results A number of tetrahydronaphthalene derivatives with MIC90 values were obtained from the literature to develop the QSAR predictive model. The compounds were divided into two sets: a training set consisting of 39 compounds and a test set containing 13 compounds. The best predictive Model 4 has R 2: 0.8303, Q 2 LOO: 0.7642, LOF: 0.0550, Q 2-F1: 0.7190, Q 2-F2: 0.7067, Q 2-F3: 0.7938 and CCCext: 0.8720. Based on the developed QSAR equation, the new compounds were designed and subjected to molecular docking, molecular dynamics and ADMET analysis. Conclusion In the QSAR model, the molecular descriptors of MATS8s, Chi4, bcutv8, Petitjeant and fr_aniline were highly influenced the antitubercular activity. The developed QSAR model helps to predict the antitubercular activity of tetrahydronaphthalene derivatives.

Keywords

• QSAR
• Tetrahydronaphthalene derivatives
• Mycobacterium tuberculosis
• ATP synthase
• Molecular docking
• MD simulation