Prediction of gas-to-olive oil partition coefficients of organic compounds using an artificial neural network.

The main aim of the present work was development of a quantitative structure-property relationship method using an artificial neural network (ANN) for predicting gas-to-olive oil partition coefficients of organic compounds. As a first step, a multiple linear regression (MLR) model was developed; the descriptors appearing in this model were considered as inputs for the ANN. These descriptors are: solvation connectivity index chi(-1), hydrophilic factor, conventional bond-order ID number, dipole moment and a total size index/weighted by atomic masses. Then a 5-5-1 neural network was generated for the prediction of gas-to-olive oil partition coefficients of 179 organic compounds including hydrocarbons, alkyl halides, alcohols, ethers, esters, ketones and benzene derivatives. The values of standard error for training, test and validation sets are 0.127, 0.122 and 0.162, respectively for ANN model. Comparisons between these values and other obtained statistical values reveal the superiority of the ANN model over the MLR one.