Quantitative structure activity relationship (QSAR) study of octanol-water partition coefficients of some of environmental toxic of petroleum substances

Life and its extraction fuels climate change. We performed studies upon an extended series of petroleum hydrocarbons, with octanol-water partition coefficients (log Kow), by using the quantitative structure-activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors, resulting in the best-fit models. The partial least squares PLS (PLS) was utilized to construct the linear QSAR model. The best GA-PLS model contains ۲۷ selected descriptors in ۱۰ latent variables space. The R۲ and RMSE for training and test sets were (۰.۸۲۷, ۰.۰۸۸) and (۰.۷۱۶, ۰.۱۸۵), respectively. Inspection of the results reveals a higher R۲ and lowers the RMSE value parameter for the data set GA-PLS. The GA-PLS linear model has good statistical quality with low prediction error. This is the first research on the QSAR which uses GA-PLS for the presiction octanol-water partition coefficients of some of the environmental toxic of the petroleum substances.

Keywords:

Petroleum substances , Genetic Algorithm , QSAR