Quantitative structure–property relationship studies of migration index in microemulsion electrokinetic chromatography using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of migration indices of the 53 benzene derivatives and heterocyclic compounds in microemulsion electrokinetic chromatography. The selected descriptors that appear in multiple linear regression models are: 3D-MoRSE signal 25 unweighted, 3D-MoRSE signal 19 weighted by atomic Sanderson electronegativity, R maximal autocorrelation index lag 1 weighted by atomic mass (R1M+ ), R maximal autocorrelation index lag 2 weighted by polarizability (R2P+ ) and average atomic composition index. These descriptors were used as inputs for generated 5-4-1 networks. After training and optimization of the ANN parameters it was used to prediction of migration index of the test set compounds. The results obtained using ANNs were compared with the experimental values as well as with those obtained using regression models and showed the superiority of ANNs over regression models.