In Silico Prediction of Antibacterial Activity of Quinolone Derivatives

The rising antimicrobial resistance crisis has diminished the effectiveness of traditional antibiotics against pathogenic bacteria. This study addresses this urgent challenge by exploring the antibacterial potential of novel quinolone derivatives (1-33). Using computational in silico modeling to simulate biological interactions, we aimed to identify candidates with potent antibacterial activity. A total of 33 quinolone derivatives were assessed for their physicochemical properties and effectiveness against a range of clinically relevant pathogens, including methicillin-resistant Staphylococcus aureus (MRSA), Klebsiella pneumoniae, Streptococcus pneumoniae, and Enterococcus faecalis. Molecular docking studies identified compounds 28, 29, 32, and 33 as having notable binding affinities, particularly against MRSA. Further molecular dynamics simulations of compound 29 confirmed its favorable stability and potential for disrupting MRSA, reinforcing the docking results and showing strong alignment with in vitro findings. These findings position compound 29 as a promising lead for developing alternative MRSA therapies and underscore the need for further in vivo studies to evaluate its therapeutic potential. The antibacterial potential of quinolone derivatives was explored using in silico methods. FMO and ED simulations were conducted via Gaussian 16 and DFT, whereas physicochemical and pharmacokinetic properties were assessed using SwissADME and ADMESAR. PyMol, Chimera, PyRx, and Gromacs performed molecular docking and dynamics. PCA analysis was completed using the Bio3D package, providing comprehensive insights. image