Computational Investigations on Inhibitors of Mycobacterium tuberculosis Shikimate Kinase: Machine Learning, Docking, Molecular Dynamics and Free Energy Calculations

Shikimate kinase emerges as an intriguing macromolecular target for the development of novel pharmaceutical agents for the treatment of tuberculosis. This study aimed to develop a neural network (NN) for the discovery of potential inhibitors of Mycobacterium tuberculosis shikimate kinase and to conduct molecular docking and molecular dynamics (MD) simulations. The NN model pointed out to a set of 810 molecules with anti-tuberculosis activity, wherein 86% of this set also demonstrated positive outcomes according to docking calculations. Among these, 54 molecules exhibited a docking score ranging from -9 to -9.8 kcal mol-1. Subsequently, a subset of molecules was selected for molecular dynamics studies and molecular mechanics Poisson- Boltzmann surface area (MM/PBSA) calculations. Furthermore, it was possible to assess that the dataset with higher affinity shared a similar electronic profile, as evidenced by the analysis of global descriptors (electronic chemical potential, hardness, and electrophilicity). The molecules displaying the lowest Gibbs free energy (AG)binding values, therefore the highest affinity, were identified as CHEMBL1229147, CHEMBL4095667, and CHEMBL120640.