QSAR study on fluoroquinolones as antibacterial agents active for Pseudomonas aeruginosa

Density functional theory (DFT) was used to calculate the properties of a set of molecular descriptors for 14 fluoroquinolone with anti-Pseudomonas aeruginosa activity. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed in order to reduce dimensionality and investigate the effectiveness of variables, i. e., which subset of variables should be more effective for classifying fluoroquinolones according to their antibacterial activities against P. aeruginosa. The PCA results showed that the variables E LUMO, ΔE HL, Q 5, Q 6, logP, MR, and MP are responsible for the separation between compounds with higher and lower anti-P. aeruginosa activity. The HCA results were similar to those obtained using PCA. By using the chemometric results, four synthetic compounds were analyzed through the PCA and HCA. Two of them are proposed as active molecules against P. aeruginosa. The result is consistent with the observations of clinic experiments. The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with anti-P. aeruginosa activity.