APPLICATION OF MULTIVARIATE IMAGE ANALYSIS - QUANTITATIVE STRUCTURE TOXICITY RELATIONSHIP STUDY FOR MODELING THE TOXICITY OF PHENOL DERIVATIVES

Abstract

The toxicity of some phenol derivatives over tetrahymena pyriformis were modeled by using multivariate image analysis (MIA) descriptors applied to quantitative structure-toxicity relationship (QSTR) method. MIA descriptors are derived from pixels of two-dimensional (2D) chemical structures that may be built by using any appropriate drawing software. QSTR analysis has been done by using partial least square regression (PLS), correlation ranking-principal component regression (CR-PCR) and correlation ranking-principal component-artificial neural network (CR-PC-ANN) modeling methods. For the ANN models two different weight update functions of Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) were investigated. Finally, obtained results using different linear and nonlinear modeling methods were compared, showing that PLS and CR-PC-ANN-SCG methods present high performance for prediction of the toxicity of phenol derivatives. Also, these results indicated that MIA descriptors may be useful to predict toxicity of phenol derivatives. Squared correlation coefficient (R 2) and standard error using the PLS method for the training set were 0.996 and 0.042 and for the test set were 0.996 and 0.048, respectively. Squared correlation coefficient (R 2) and standard error using the CR–PC–ANN-SCG method were respectively, 0.999 and 0.002 for the training set, 0.999 and 0.001 for the test set and 0.999 and 0.002 for the validation set.