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pK<inf>a</inf> modeling and pbkp_rediction of a series of pH indicators through genetic algorithm-least square support vector regression

Abstract:

The pKa values of a series of 107 indicators have been modeled by means of a quantitative structure-property relationship (QSPR) approach based on physicochemical descriptors and different variable selection and regression methods. A genetic algorithm/least square support vector regression (GA-LSSVR) model gave the most accurate estimations/pbkp_redictions, with squared correlation coefficients of 0.90 and 0.89 for the training and test set compounds, respectively. The pbkp_rediction ability of this model was found to be superior to that based on support vector machine regression alone, revealing the important effect of selecting suitable descriptors during a QSPR modeling. Moreover, the GA-LSSVR model showed higher pbkp_redictive capability than linear methods, demonstrating the influence of nonlinearity on the modeling of pKa values, an extremely useful parameter in the analytical sciences. © 2010 Elsevier B.V.