Three Dimensional Quantitative Structure Activity Relationship (QSAR) Analysis on Arylbenzofuran Derivatives as Histamine H3 Antagonists using k-Nearest Neighbor Molecular Field Analysis

A three dimensional quantitative structure activity relationship (3D QSAR) using k nearest neighbor molecular field analysis (kNN MFA) method was performed on a series of arylbenzofuran derivatives as H3-receptor antagonists. This study was performed with 29 compounds (data set) using sphere exclusion (SE) algorithm and random selection method for the division of the data set into training and test set. kNN-MFA methodology with stepwise (SW), simulated annealing (SA) and genetic algorithm (GA) was used for building the QSAR models. A predictive model was generated with SW-kNN MFA having internal predictivity 70.55% (q2 = 0.7055) and external predictivity 60.00 % (pred_r2 = 0.60). Model showed that steric (S_579), electrostatic (E_453) and hydrophobic (H_779) interactions play important role in determining H3-receptor antagonistic activity. The kNN-MFA contour plots provided further understanding of the relationship between structural features of substituted arylbenzofuran derivatives and their activities which should be applicable to design newer potential H3-receptor antagonists.