| Title: | Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network |
| Address: | "Department of Chemistry, Central Tehran Branch, Islamic Azad University, Tehran, Iran" |
| DOI: | 10.1016/j.aca.2008.04.065 |
| ISSN/ISBN: | 1873-4324 (Electronic) 0003-2670 (Linking) |
| Abstract: | "An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) features selection techniques. These descriptors are: R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately" |
| Keywords: | "Air/*analysis *Algorithms *Models, Genetic *Neural Networks, Computer Organic Chemicals/*blood Volatilization;" |
| Notes: | "MedlineKonoz, Elahe Golmohammadi, Hassan eng Research Support, Non-U.S. Gov't Netherlands 2008/06/19 Anal Chim Acta. 2008 Jul 7; 619(2):157-64. doi: 10.1016/j.aca.2008.04.065. Epub 2008 May 13" |
