PARENT SESSION

1F - QSAR
Poster Hall
8:30 AM - Tuesday, 29 April 2003
Chair: Schüürmann, G.¹, ¹
Co-chair: Verhaar, H.J.M.², Cronin, M.³, ^{2 3}

(TUP/48) QSAR Prediction of Physico-Chemical Properties of Esters.

Gramatica, Paola¹, Battaini, Francesca¹, Papa, Ester¹, ¹ QSAR and Environmental Chemistry Research Unit - University of Insubria, Varese, VA, Italy

ABSTRACT- The behaviour and fate of chemicals in the environment is strongly influenced by the inherent properties of the compounds themselves, particularly by the basic physico-chemical properties such as solubility in water, vapour pressure, melting point, boiling point, flash point and density. The knowledge of physico-chemical properties is of fundamental interest in risk assessment studies, and is a specific requirement of the EU-Directive White Paper on a strategy for a future Community Policy for Chemicals for existing chemicals, particularly for High Production Volume (HPV) compounds. In this paper a data set of 153 esters has been studied. The application of the Genetic Algorithm as Variable Subset Selection (GA-VSS) to a wide set of theoretical molecular descriptors of different structural aspects, like 1D-, 2D- and 3D-descriptors (DRAGON software), produces highly predictive models of the studied physico-chemical properties. The best linear models, obtained by Ordinary Least Squares regression (OLS) were validated for predictivity both internally, using leave-one-out (Q² LOO=78-94%), leave-many-out (30-50%), Y-scrambling and externally (Q² EXT =88-94% except for the melting point model). The splitting of the data set into a training and an evaluation set was realised by D-optimal Experimental Design. The reliability of the predictions was checked by the leverage approach in order to verify the chemical domain of the models. The application of the proposed class-specific QSAR models allows fast knowledge of the physico-chemical properties of existing esters. This approach could also be applied usefully to new chemicals, even those not yet synthesised, as it is based simply on the knowledge of the molecular structure.

Key words: esters, QSAR, physico-chemical properties, external validation