The Comparative Molecular Surface Analysis (COMSA) − A Nongrid 3D QSAR Method by a Coupled Neural Network and PLS System: Predicting p<i>K</i>_a Values of Benzoic and Alkanoic Acids

Abstract

A self-organizing neural network was used to design a novel method capable of the quantitative prediction of molecular properties. The method is based on the comparison of molecular surfaces performed by the coupled neural network and PLS system. Unlike CoMFA and related methods it does not compare the properties describing a discrete set of points but the average property values calculated for a certain area of the molecular surface. It has been found that the results of the PLS analysis of the series of the comparative matrices of the molecular electrostatic potential (MEP) are quite stable. Also the results only slightly depend on such parameters as the number of points sampled at the molecular surface (D) or a winning distance (MD) of the self-organizing neurons. The influence of these parameters for modeling the effects limited by steric and electronic effects was determined and the pK(a) values of the ortho-, meta-, and para- (o-, m-, p-) analogues of benzoic acid and selected alkanoic acids were predicted. We generally found that for the series analyzed CoMSA gave better models than CoMFA.