Artificial Intuition and accelerating the process of antimicrobial drug discovery

New drug development is a very challenging, expensive, and usually time-consuming process. This issue is very important with regard to antimicrobials, which are affected by the global issue of the development and spread of resistance. This framework underscores the urgency of accelerating drug development processes while reducing their costs. In this context, new bioinformatics tools can provide important support for drug development by limiting and shortening in vitro evaluation of the best outcomes, thereby minimizing costs. Recently, new Artificial Intelligence (AI)-based tools have been developed for de novo design of new molecules, or for the identification of features of inhibitors among a large set of molecules that can guide rational design. With this work, we present an Artificial Intuition (AI4)-based pharmacological analysis of a series of antimicrobial compounds that are known to be active against Mycobacterium tuberculosis. The compounds have been subjected to Molecular Dynamic Simulation (MDS), and the respective outputs processed with a Quantitative Complexity Management (QCM) tool in order to determine the corresponding complexity profiles. The comparison of different analogues in each series revealed a relationship between the complexity of the various chemical moieties and their importance for the biological activity of each compound, suggesting that QCM may be a useful tool in guiding the optimization process. This first attempt to apply the tool in the field of drug development has yielded interesting results, indicating that QCM, which powers AI4, can be implemented for rational drug design in the near future.