Please login first
Atom-Based Quadratic Indices to Predict Aquatic Toxicity of Benzene Derivatives to Tetrahymena pyriformis
* 1, 2, 3 , 1 , 2, 3 , 3
1  Applied Chemistry Research Center, Faculty of Chemistry-Pharmacy and Department of Drug Design, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
2  Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemistry-Pharmacy, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
3  Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P. O. Box 22085, 46071 Valencia, Spain

Abstract: The non-stochastic and stochastic atom-based quadratic indices are applied to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity. The used dataset, consisting of 392 benzene derivatives for which toxicity data to the ciliate Tetrahymena pyriformis were available, is divided into training and test sets. The obtained multiple linear regression models are statistically significant (R2 = 0.787 and s = 0.347, R2 = 0.806 and s = 0.329, for non-stochastic and stochastic quadratic indices, respectively) and show rather good stability in a cross-validation experiment (q2 = 0.769 and scv = 0.357, q2 = 0.791 and scv = 0.337, correspondingly). In addition, a validation through an external test set is performed, which yields significant values of R2 pred of 0.745 and 0.742. The comparison with other approaches exposes a good behavior of our method of predicting the aquatic toxicity of benzenes. The obtained results suggest that, the non-stochastic and stochastic quadratic indices seem to provide an interesting alternative to costly and timeconsuming experiments for determining toxicity.
Keywords: Atom-based non-stochastic and stochastic linear index, Multiple linear regression, QSAR, Tetrahymena pyriformis, Program TOMOCOMD-CARDD