Comprehensive 2D-Quantitative Structure-Activity Relationship Study on Monobactam Analogues Against Gram-Negative Bacteria

Abstract

Human health has been severely affected by infections resulting from multidrug-resistant (MDR) gram-negative bacteria (GNB). Monobactam antibiotics are known to be effective against such infections. This study aimed to construct a predictive two-dimensional quantitative structure-activity relationship (2D-QSAR) model for the rational design of new monobactams based on the 65 known monobactams against Escherichia coli (Eco) and Klebsiella pneumonia (Kpn) strains using the kernel partial least squares regression (KPLS) algorithm. The total performance of Eco and Kpn KPLS modes was shown as RMSE: 0.681/0.596, R2: 0.946/0.882, Q2: 0.922/0.877, and RMSU: 0.625/0.593. Thirty-four monobactams reported in our lab were chosen as external data to predict their activities against Eco and Kpn using the newly established models, by which the R2 between the experimental and predicted values was 0.878 and 0.871, respectively. The models developed and verified in this study provide a powerful design strategy for novel monobactams that are effective against MDR gram-negative bacterial infections.