PREDICTING REDUCTIVE TRANSFORMATION RATES OF HALOGENATED ALIPHATIC-COMPOUNDS USING DIFFERENT QSAR APPROACHES

The kinetics of the reductive transformation rates of a set of 17 halo genated aliphatic hydrocarbons in anaerobic sediment-water mixtures ar e examined using different QSAR methods. Statistical experimental desi gn in combination with multivariate chemical characterization of the c ompounds was used to select a representative training and validation s et. The aim of the QSARs is to generate predictions for priority setti ng and risk assessment purposes, and to better understand the kinetics of the dehalogenation of aliphatic hydrocarbons. The first QSAR was c onstructed with multiple linear regression using readily available des criptors. Subsequently, a multivariate QSAR was constructed using the partial least squares (PLS) method with 36 (physico)-chemical descript ors. Finally, a transition state approach has been used in which quant um chemically calculated activation energies for the transition state of the most probable reaction mechanism are used to model the reaction rate constants k. Because of the relatively small size of the trainin g set (10 compounds) the linear regression QSAR using multiple descrip tors does not show good predictive capabilities on the validation set. The PLS relationship and the transition state QSAR are both capable o f generating predictions of rate constants within one order of magnitu de. Moreover, the transition state QSAR closely follows, and thus corr oborates the assumed reaction mechanism for reductive dehalogenation. Predictions for 23 non tested halogenated aliphatics are given and com pared using both the PLS and the transition state model.