Quantitative Structure-Electrochemistry Relationship Study of Some Organic Compounds Using PC-ANN and PCR

Abstract

Motivation. A QSPR analysis has been conducted on the half-wave reduction potential (E1/2) of a diverse set of organic compounds by means of principal component regression (PCR) and principal component-artificial neural network (PC-ANN) modeling method. Genetic algorithm was employed as a factor selection procedure for both modeling methods. The results were compared with two other factor selection methods namely eigen-value ranking (EV) and correlation ranking (CR) procedures. Method. By using the Dragon software more than 1000 structural descriptors were calculated for each molecule. The descriptor data matrix was subjected to principal component analysis and the most significant principal components (PC) were extracted. Multiple linear regression and artificial neural network were employed for the respective linear and nonlinear modeling between the extracted principal components and E1/2. First, the principal components were ranked by decreasing eigen-values and entered successively to each modeling method separately. In addition, the factors were ranked by their corresponding correlation (linear correlation for PCR and nonlinear correlation for PC-ANN models) with the half-wave potentials and entered to the models. Finally, genetic algorithm (GA) was also employed to select the best set of factors for both models. Results. The 96 % of variances in the descriptor data matrix could be explained by 30 first extracted PCs. Amon