GCN-Based Structure-Activity Relationship and DFT Studies of Staphylococcus aureus FabI Inhibitors

Abstract

The enoyl-[acyl-carrier-protein] reductase (FabI) is an important enzyme in the fatty acid metabolism of Gram-positive bacteria, such as Staphylococcus aureus. FabI is also a potential target for the development of novel antibacterials. Several machine learning-driven studies were reported to develop FabI inhibitors, describing robust and predictive models. Herein, the authors applied the kGCN, a graph convolutional network framework, to generate classification models to select potential S. aureus FabI inhibitors. The most predictive model showed robustness for both active and inactive class prediction, according to statistical validation. Finally, the chemical interpretation of the model was consistent with prior experimental and theoretical works. The SAR analysis highlighted the importance of the occupation of hydrophobic pockets and polar interactions with Tyr-156 and NADPH cofactor present in the FabI catalytic site by potential inhibitors. A density functional theory study endorsed the SAR, where the electrostatic surfaces were consistent with the expected interactions with the pocket.