Computational modeling and ligand-based design of some novel hypothetical compound as prominent inhibitors against Mycobacterium tuberculosis

Shola Elijah Adeniji,Olajumoke Bosede Adalumo

doi:10.1186/s43094-020-00027-z

Abstract

BackgroundTime consumed and expenses in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multi-drug-resistant strain Mycobacterium tuberculosis (TB). To solve the above problem, quantitative structure activity relationship (QSAR) is a recent approach developed to discover novel agents with better biological activity against M. tuberculosis.ResultsA validated QSAR model was developed in this study to predict the biological activities of some anti-tubercular compounds and to design new hypothetical drugs is influenced with the molecular descriptors, AATS7s, VR1_Dzi, VR1_Dzs, SpMin7_Bhe, and TDB8e, which has been validated through internal and external validation test. Prior to high anti-tubercular activity of the lead compound, compound 17 served as a template structure to design compounds with improved activity. Among the compounds designed, compounds 17i, 17j, and 17n were observed with improved anti-tubercular activities which ranges from 8.8981 to 9.0377 pBA.ConclusionThe outcome of this research is recommended for pharmaceutical and medicinal chemists to synthesis and carry out an in vivo and in vitro screening for the proposed designed compounds in order to substantiate the computational findings.

Highlights

Time consumed and expenses in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multi-drug-resistant strain Mycobacterium tuberculosis (TB)
The strain energy from the molecules was removed by employing molecular mechanics force field (MMFF), and complete optimization was achieved with the aid of density functional theory (DFT) by utilizing the (B3LYP) basic set [4]
EE is the standard error of estimation, w is the total number of terms present in the built model except the constant term, j is the number of descriptors confined in the built model, q is a user-defined factor, and N is the number of compounds of training set

Summary

Introduction

Time consumed and expenses in discovering and synthesizing new hypothetical drugs with improved biological activity have been a major challenge toward the treatment of multi-drug-resistant strain Mycobacterium tuberculosis (TB). M. tuberculosis toward the aforementioned drugs has led to advances in searching for new and better approach that is precise and fast in developing a novel compound with improved biological activity against M. tuberculosis. Computational methods which reduced the cost for effective evaluation of large virtual database of chemical compounds are currently employed in designing new drugs. Such method includes complex network theory, quantitative structure–activity relationships (QSAR) models, Machine Learning (ML), and Artificial Neural Networks (ANN) analysis.

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