A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models.
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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