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Prediction of logkwof disubstituted benzene derivatives in reversed-phase high-performance liquid chromatography using multiple linear regression and radial basis function neural network

Authors
Journal
Analytica Chimica Acta
0003-2670
Publisher
Elsevier
Publication Date
Volume
463
Issue
1
Identifiers
DOI: 10.1016/s0003-2670(02)00376-8
Keywords
  • Neural Network
  • Radial Basis Function
  • Quantitative Structure–Retention Relationships
  • High-Performance Liquid Chromatography
Disciplines
  • Chemistry
  • Computer Science
  • Physics

Abstract

Abstract A study of the relationships between the extrapolated capacity factor (log k w) of a group of 54 disubstituted benzene derivatives and a set of eight molecular descriptors was made. By using multiple linear regression (MLR), we obtained an empirical function, which included five descriptors. The performance of a radial basis function neural network (RBFNN) was evaluated. The network used thin plate spline and multi-quadratic functions, which showed better than MLR. Semi-empirical quantum chemical method PM3 implemented in HyperChem 4.0 was employed to calculate the molecular descriptors of the compounds. The results gave a relative minor root mean squared (rms) error (0.070 and 0.084) and indicated that the quantitative structure–retention relationships (QSRR) models proposed were very satisfactory.

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