Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1227960 | Microchemical Journal | 2010 | 12 Pages |
Abstract
A quantitative structure-property relationship (QSPR) study based on an artificial neural network (ANN) was carried out for the prediction of the gas-to-chloroform partition coefficients of a set of 338 compounds of a very different chemical nature. The genetic algorithm-partial least squares (GA-PLS) method was used as a variable selection tool. A PLS method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. These descriptors are Gravitation index for all bonded pairs of atoms (G2), Final heat of formation (ÎHf), Total hybridization components of the molecular dipole (µh), DPSA-3 Difference in CPSAs (DPSA-3) and Structural Information content (order 1) (1SIC). The results obtained showed the ability of developed artificial neural networks to predict of gas-to-chloroform partition coefficients of various compounds. Also this demonstrates the advantages of ANN.
Keywords
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Hassan Golmohammadi, Majid Safdari,