Quantitative structure–activity relationship methods in the discovery and development of antibacterials

With the pressing issue of antibiotic resistance, there is a constant need for new antibiotics. However, the fact that traditional methods of drug discovery are expensive and time‐consuming has discouraged the pharmaceutical industry, leaving the burden of discovery to research institutions. This is where quantitative structure–activity relationship (QSAR) methods become a key tool in fighting multidrug‐resistant bacteria, seeing as they provide useful information for the rational design of new active molecules at a minimal cost. A variety of linear and nonlinear statistical methods are used to develop these models based on the 2D or 3D representations of the molecules. QSAR models have proven to be effective in rapidly providing lead compound candidates against resistant bacteria such as methicillin‐resistant Staphylococcus aureus, Escherichia coli, Pseudomonas spp., Bacillus subtilis, or Mycobacterium tuberculosis. Moreover, QSAR methods allow for a deeper analysis of a library of molecules, selecting those with not only the optimal activity, but also the most favorable pharmacokinetic and toxicological profiles. The information obtained from QSAR studies makes optimizing an existing drug simpler, which is a cost‐effective approach to obtain new treatments against increasingly resistant bacteria. This article is categorized under: Computer and Information Science > Chemoinformatics Software > Molecular Modeling QSAR methods allow for a deep analysis of a library of molecules, selecting those with not only the optimal activity, but also the most favorable pharmacokinetic and toxicological profiles, which is a cost‐effective approach to obtain new treatments against increasingly resistant bacteria.