quantitative structure-retention relationships applied to chromatographic retention of ecotoxicity of anilines and phenols

Aniline, phenol, and their derivatives are widely used in industrial chemicals that consequently have a high potential for environmental pollution. genetic algorithm and partial least square (ga-pls), kernel partial least square (ga-kpls) and levenberg-marquardt artificial neural network (l-m ann) techniques were used to investigate the correlation between chromatographic retention (log k) and descriptors for modelling the toxicity to fathead minnows of anilines and phenols. descriptors of ga-pls model were selected as inputs in l-m ann model. the described model does not require experimental parameters and potentially provides useful prediction for log k of new compounds. finally a model with a low prediction error and a good correlation coefficient was obtained by l-m ann. the stability and prediction ability of l-m ann model was validated using external test set techniques.