Quantitative Structure Interaction Activity Relationship (QSIAR) as a Novel Approach to Drug Design: A Case Study of Anti-tubercular Agents

Computer-aided drug design (CADD)—an effective tool in the process of drug design and discovery—consists of ligand-based drug design (LBDD) and structure-based drug design (SBDD). The SBDD has gained importance in recent years due to the developments in molecular biology including genomics, proteomics, and structural information of new targets. Docking scores play a key role in analysing the results in terms of interactions between the structural components of the interacting molecule with the target protein. The docking scores generally do not correlate with the observed biological activity. This may be attributed to the limitations in scoring functions used in docking algorithms that often fail to account for crucial factors like entropy change, solvation effects, and interactions, contributing to binding affinity limiting the accurate prediction of binding energy changes, which may result in the poor correlations between observed biological activity and docking scoring functions. In this context, a maiden novel approach (quantitative structure interaction activity relationship (QSIAR)) has been applied to address this issue by taking into account the specific interactions between a ligand and the amino acid residues present at the active site as independent and biological activity as dependent parameter(s) in quantitative terms to explain the observed anti-tubercular activity as mycobacterium ATP synthase inhibitory activity in diverse molecules such as 4-substituted amino sulphonyl-2-methyl-7-chloroquinolines, bisquinoline, imidazo[1,2-a]pyridine ethers, and squaramides. The developed and validated quantitative model(s) have led to the identification of novel leads through virtual screening of the focussed libraries for the development of new anti-tubercular agents.