Prediction of air to liver partition coefficient for volatile organic compounds using QSAR approaches

In this work a quantitative structure-activity relationship (QSAR) technique was developed to investigate the air to liver partition coefficient (log K liver) for volatile organic compounds (VOCs). Suitable set of molecular descriptors was calculated and the important descriptors were selected by GA-PLS methods. These variables were served as inputs to generate neural networks. After optimization and training of the networks, they were used for the calculation of log K liver for the validation set. The root mean square errors for the neural network calculated log K liver of training, test, and validation sets are 0.100, 0.091, and 0.112, respectively. Results obtained reveal the reliability and good predictivity of neural network for the prediction of air to liver partition coefficient for volatile organic compounds. Plot of calculated air to liver partition coefficient against experimental values. [Display omitted]