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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.