QSAR based therapeutic management of M. tuberculosis

Abstract

Mycobacterium tuberculosis is responsible for severe mortality and morbidity worldwide but, under-developed and developing countries are more prone to infection. In search of effective and wide-spectrum anti-tubercular agents, interdisciplinary approaches are being explored. Of the several approaches used, computer based quantitative structure activity relationship (QSAR) have gained momentum. Structure-based drug design and discovery implies a combined knowledge of accurate prediction of ligand poses with the good prediction and interpretation of statistically validated models derived from the 3D-QSAR approach. The validated models are generally used to screen a small combinatorial library of potential synthetic candidates to identify hits which further subjected to docking to filter out compounds as novel potential emerging drug molecules to address multidrug-resistant tuberculosis. Several newer models are integrated to QSAR methods which include different types of chemical and biological data, and simultaneous prediction of pharmacological activities including toxicities and/or other safety profiles to get new compounds with desired activity. In the process, several newer molecules have been identified which are now being assessed for their clinical efficacy. Present review deals with the advances made in the field highlighting overall future prospects of the development of anti-tuberculosis drugs.

Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds

“…This study was based on an analysis of the SAR of the pharmacophore model. The SAR describes the relationship between the chemical properties/three-dimensional structure of the compound and its biological activity, and researchers can identify the functional groups that play key roles in activities of small molecules (Ahamad et al, 2017). Any subsequent studies, such as the design of new compounds, structural modification of compounds, drug screenings and prediction of active compounds, are based on SARs.…”

Tackling Multiple-Drug-Resistant Bacteria With Conventional and Complex Phytochemicals

“…To o v e r c o m e t h e t i m e c o n s u m p t i o n o f t h e s e a c t i v e phytocompounds as a drug on the market as a part of preclinical studies, in silico approach with natural phytocompounds were chosen on the basis of its bioactive constituents. Besides to interpret the characteristics of molecular structures such as the interactions of protein-ligand binding, an analysis of the quantitative structure activity by QSAR helps predict the compound with a specific target (Ahamad et al, 2017). Similarly, studies on pharmacophore models that simulate the 3D arrangements of particles with various physicochemical features are tangled in the interaction between ligand and target.…”

Plant-Based Phytochemicals as Possible Alternative to Antibiotics in Combating Bacterial Drug Resistance

“…In addition, to predict characteristics of molecular structures like protein-ligand biding interactions, in silico simulations can be used. Various computational approaches have been reported, such as the QSAR model (quantitative structure activity relationship); [71,72], which is used to predict compounds with high specificity for targets. Bioactivity databases, such as CHEMBL [73] or PubChem [74] are used to evaluate datasets for compatibility among naturally obtained products and synthetically obtained pharmacologically active molecules.…”