QSPR analysis of air-to-blood distribution of volatile organic compounds

Quantitative structure property relationship (QSPR) models for the prediction of human blood:air partition coefficient (log K blood) of volatile organic compounds (VOCs) has been developed based on the linear heuristic method (HM) and non-linear radial basis function neural networks (RBFNNs). Molecu...

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Bibliographic Details
Published in:Ecotoxicology and environmental safety 2008-11, Vol.71 (3), p.731-739
Main Authors: Luan, F., Liu, H.T., Ma, W.P., Fan, B.T.
Format: Article
Language:English
Subjects:
Air
Air Pollutants - blood
Air Pollutants - metabolism
Humans
Linear Models
Models, Biological
Models, Chemical
Neural Networks (Computer)
Nonlinear Dynamics
QSPR
Quantitative Structure-Activity Relationship
RBFNN
Solubility
VOCs
Volatile Organic Compounds - blood
Volatile Organic Compounds - metabolism
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Summary:Quantitative structure property relationship (QSPR) models for the prediction of human blood:air partition coefficient (log K blood) of volatile organic compounds (VOCs) has been developed based on the linear heuristic method (HM) and non-linear radial basis function neural networks (RBFNNs). Molecular descriptors that are calculated from the structures alone were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. RBFNN was performed to obtain more accurate models. Both the linear and the non-linear models can give very satisfactory prediction results: the correlation coefficient R was 0.964 and 0.979, and the root-mean-square (RMS) error was 0.3303 and 0.2542 for the whole data set, respectively. The prediction result of the non-linear model is better than that obtained by the linear model. In addition, this paper provides an effective method for predicting log K blood from its structures and gives some insight into the structural features related to the solubility of VOCs in human blood.
ISSN:0147-6513
1090-2414
DOI:10.1016/j.ecoenv.2007.10.024