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Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network

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Abstract

Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of the basicity constant (pK b ) for various pyridines (91 compounds) dissolved in water at 25°C. A large number of theoretical descriptors were calculated for each compound. The first 54 principal components (PCs) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PCs, the genetic algorithm was employed for selection of the best set of extracted PCs for PC-MLR and PC-ANN models. The models were generated using eight principal components as variables. For evaluation of the predictive power of the models, pK b values of 18 compounds in the prediction set were calculated. Mean percentage deviation (MPD) for PC-GA-MLR and PC-GA-ANN models are 21.096 and 3.541. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN model relative to the PC-GA-MLR model. The improvements are due to the fact that the pK b of the pyridines demonstrate non-linear correlations with the principal components.

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Science, University of Mohaghegh Ardabili, Ardabil, Iran

    Aziz Habibi-Yangjeh, Eslam Pourbasheer & Mohammad Danandeh-Jenagharad

Authors
  1. Aziz Habibi-Yangjeh
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  2. Eslam Pourbasheer
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  3. Mohammad Danandeh-Jenagharad
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Correspondence to Eslam Pourbasheer.

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Correspondence: Eslam Pourbasheer, Department of Chemistry, Faculty of Science, University of Mohaghegh Ardabili, Ardabil, Iran.

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Habibi-Yangjeh, A., Pourbasheer, E. & Danandeh-Jenagharad, M. Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network. Monatsh Chem 139, 1423–1431 (2008). https://doi.org/10.1007/s00706-008-0951-z

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