Application of PCA and HCA to the Structure-activity Relationship(SAR) Study of Fluoroquinolones

Density functional theory (DFT) was used to calculate a set of molecular descriptors (properties) for 14 fluoroquinolones with anti-B.fragilis activity. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed in order to reduce dimensionality and investigate which subset of variables should be more effective for classifying fluoroquinolones according to their an-B.fragilis activity degree. The PCA shows that the variables of ELUMO, ’EHL, μ, Q2, Q3, Q5, Q6, QB, LogP, MR and MP are responsible for the separation between compounds with higher and lower anti-B.fragilis activities. The HCA results are similar to those obtained with PCA. By using the chemometric results, 4 synthetic compounds were analyzed through PCA and HCA, and 2 of them are proposed as active molecules against B.fragilis, which is consistent with the results of clinic experiments. The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with anti-B.fragilis activity.