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Prediction of electrophoretic mobilities of alkyl- and alkenylpyridines in capillary electrophoresis using artificial neural networks

Jalali Heravi, M ; Sharif University of Technology | 2002

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  1. Type of Document: Article
  2. DOI: 10.1016/S0021-9673(02)01043-9
  3. Publisher: 2002
  4. Abstract:
  5. The electrophoretic mobilities of 31 isomeric alkyl- and alkenylpyridines in capillary electrophoresis were predicted using an artificial neural network (ANN). The multiple linear regression (MLR) technique was used to select the descriptors as inputs for the artificial neural network. The neural network is a fully connected back-propagation model with a 3-6-1 architecture. The results obtained using the neural network were compared with those obtained using the MLR technique. Standard error of training and standard error of prediction were 6.28 and 5.11%, respectively, for the MLR model and 1.03 and 1.20%, respectively, for the ANN model. Two geometric parameters and one electronic descriptor that were used as inputs in the ANN are able to distinguish between the isomers. © 2002 Elsevier Science B.V. All rights reserved
  6. Keywords:
  7. Alkenylpyridines ; Alkylpyridines ; Electrophoretic mobility ; Molecular descriptors ; Neural networks, artificial ; Pyridines ; Regression analysis ; Structure-activity relationships
  8. Source: Journal of Chromatography A ; Volume 971, Issue 1-2 , 2002 , Pages 207-215 ; 00219673 (ISSN)
  9. URL: https://linkinghub.elsevier.com/retrieve/pii/S0021967302010439