QSAR modelling on a series of arylsulfonamide-based hydroxamates as potent MMP-2 inhibitors.

Matrix metalloproteinase-2 (MMP-2) is a lucrative therapeutic target as far as anticancer drug discovery is concerned. Overexpression of MMP-2 is found to facilitate tumour propagation through the involvement of vascular endothelial growth factor (VEGF). However, even after different techniques, finding a target-specific MMP-2 inhibitor with respectable pharmacodynamic properties is still a challenging task. Regression-dependent quantitative structure-activity relationship (QSAR) strategies might be among the possible drug design methods to explore the essential structural features that would be valuable to find a suitable MMP-2 inhibitor. In this paper, 72 molecules were explored using the PaDEL descriptors and stepwise multiple linear regression (S-MLR). The partial least squares (PLS) method was also used to create a viable statistical model with an acceptable metric related to these models. The final statistical models were formed with statistical parameters within acceptable range (r2 = 0.797, Q2 = 0.725 and r2pred = 0.643 for the MLR model, and r2 = 0.780, Q2 = 0.685 and r2pred = 0.666 for the PLS model). The models were analysed and compared with those already published on the same endpoint.