Cluster analysis and two-dimensional quantitative structure–activity relationship (2D-QSAR) of Pseudomonas aeruginosa deacetylase LpxC inhibitors

Compounds from a wide variety of structural classes inhibit Pseudomonas aeruginosa deacetylase LpxC. However, a single unified understanding of the relationship between the structures and activities of these compounds still eludes the researchers. We report herein, the development of cluster analysis-based 2D-QSAR models for LpxC inhibition. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and genetic function approximation (GFA) were employed for the development of the QSAR model. The conventional 2D-QSAR model derived for the complete set of three-structural classes had unsatisfactory predictability with a correlation coefficient (r2) of 0.703 and a cross-validated correlation coefficient (q2) of 0.584. Descriptor-based cluster analysis indicated that the three-structural classes of LpxC inhibitors studied belonged to two clusters. Separate QSAR models for these two clusters showed substantially improved predictability with r2 values of 0.904 and 0.944 and q2 values of 0.805 and 0.906, respectively. Thus, we expect that compared to the conventional model, our two QSAR models can be better used to preliminarily screen molecules from a diverse chemical space while searching for novel LpxC inhibitors.

Conventional and cluster analysis based methods for QSAR development have been analysed and compared for their predictability on a set of Pseudomonas aeruginosa Deacetylase LpxC inhibitors. Cluster analysis based approach proved to be better than the conventional technique.Figure optionsDownload as PowerPoint slide