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 Kliver) 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 Kliver for the validation set. The root mean square errors for the neural network calculated log Kliver 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.
Department of Chemistry, Islamic Azad University, Science and Research Branch, Young researchers club, Tehran, Iran.
Zahra
Z
Dashtbozorgi
Hassan
H
Golmohammadi
D000388:Air; D016018:Least-Squares Analysis; D008099:Liver / Q000378:metabolism*; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry* / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D014835:Volatilization
D009930:Organic Chemicals