Producing Highly Predictive Qsar Models Of Benzimidazole Derivatives As Potent Antifungal Agents

Abstract

The antifungal efficacy of freshly synthesized Aryl Benzimidazole derivatives was investigated using quantitative structure-activity relationship (QSAR) analysis. Molecular Design Suite was used to create the statistically significant 2D-QSAR models (r² = 0.8361; q² = 0.7457; F test = 35.71; r²se = 0.0883; q²se =0.1100; pred r² = 0.6511; pred r²se = 0.2360). (VLifeMDS 4.3.1) The data set for the study consisted of 36 chemicals, and the training and test sets were created using the sphere exclusion (SE) algorithm, random selection, and manual selection techniques. The QSAR models were constructed using multiple linear regression (MLR) methodologies and the stepwise (SW) forward-backward variable selection method.To investigate the substitutional requirements for the favorable antifungal activity against Candida albicans and to provide useful information in the characterization and differentiation of their binding sites, the results of the 2D-QSAR models were further compared with 3D-QSAR models produced by kNN-MFA (kNearest Neighbor Molecular Field Analysis). The findings could help with future designs for synthesizing aryl benzimidazole derivatives of benzimidazole.