In silico studies and the design of novel agents for the treatment of systemic tuberculosis

Abstract

Tuberculosis (TB) is an ancient infectious disease, which re-emerged with the appearance of multidrug-resistant strains and acquired immune deficiency syndrome. Enoyl-acyl-carrier protein reductase (InhA) has emerged as a promising target for the development of anti-tuberculosis therapeutics. This study aims to develop quantitative structure-activity relationship (QSAR) models for a series of arylcarboxamides as InhA inhibitors. The QSAR models were calculated on the basis of optimal molecular descriptors based on the simplified molecular-input line-entry system (SMILES) notation with the Monte Carlo method as a model developer. The molecular docking study was used for the final assessment of the developed QSAR model and designed novel inhibitors. Methods used for the validation indicated that the predictability of the developed model was good. Structural indicators defined as molecular fragments responsible for increases and decreases of the studied activity were defined. The computer-aided design of new compounds as potential InhA inhibitors was presented. The Monte Carlo optimization was capable of being an efficient in silico tool for developing a model of good statistical quality. The predictive potential of the applied approach was tested and the robustness of the model was proven using different methods. The results obtained from molecular docking studies were in excellent correlation with the results from QSAR studies. This study can be useful in the search for novel anti-tuberculosis therapeutics based on InhA inhibition.

Communicated by Ramaswamy H. Sarma

Keywords:

• InhA inhibitors
• anti-tuberculosis therapy
• arylcarboxamides
• QSAR
• molecular modeling

Disclosure statement

We have no conflicts of interest to disclose.

Supplementary material

The supplementary material for this article is available online at http://dx.doi.org/10.1080/07391102.2018.1511476.

Additional information

Funding

This work is based upon work from COST Action CA15135 and supported by the Ministry of Education and Science, the Republic of Serbia, under Project Number 43012.