Modeling Robust QSAR, 2. Iterative Variable Elimination Schemes for CoMSA: Application for Modeling Benzoic Acid pKa Values

A variety of issues decide the efficiency of 3D QSAR methods, and their practical importance for drug design is still controversial. This refers both to the predictive ability and the possibility for the indication of these areas within 3D molecular representations that are responsible for biological or chemical effects. Technically, the latter comes down to the selection or elimination of the reliable variables during 3D QSAR modeling using the Partial Least-Squares (PLS) method. In this paper we used a series of benzoic acids to test the dependence between the predictive ability and variable selection performance of PLS with Iterative Variable Elimination (IVE-PLS) in the Comparative Molecular Surface Analysis (CoMSA) modeling of Hammett constant which correlates with the pKa values. Modeling this chemical effect allowed us to select the IVE-PLS variant that plots the contour maps indicating a carboxylic function, i.e., the region including the dissociation reaction center that determines the respective pKa values. In fact, it appeared that a novel robust IVE version is capable of the indication of the proper contour plots independent of the method used for the calculation of partial atomic charges (AM1 or Gasteiger-Marsili).