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• W1983707632 abstract "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." @default.
• W1983707632 created "2016-06-24" @default.
• W1983707632 creator A5052837100 @default.
• W1983707632 creator A5074966085 @default.
• W1983707632 date "2010-06-01" @default.
• W1983707632 modified "2023-10-16" @default.
• W1983707632 title "Prediction of air to liver partition coefficient for volatile organic compounds using QSAR approaches" @default.
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• W1983707632 doi "https://doi.org/10.1016/j.ejmech.2010.01.056" @default.
• W1983707632 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20153567" @default.
• W1983707632 hasPublicationYear "2010" @default.
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