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A Quantitative Structure-Property Relationship (QSPR) study based on artificial neural network (ANN) was carried out for the prediction of the gas-to-wet butyl acetate partition coefficient of a set of 81 organic compounds of very different chemical nature. The genetic algorithm-partial least squares (GA-PLS) method was applied as a variable selection tool. A PLS method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. These descriptors are: Randic index (order 2) ( 2 v), atomic charge weighted partial positively charged surface area (PPSA-3), difference between atomic charge weighted partial positive and negative surface areas (DPSA-3), minimum net atomic charge for a O atom (q min O) and hydrogen bonding donor ability of the molecule (HDSA1). The results obtained showed the ability of developed ANN for prediction of the gas-to-wet butyl acetate partition coefficients of various compounds. Also result reveals the superiority of the ANN over the PLS model.

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