Tham, S.Y., and Agatonovic-Kustrin, S. (2002) Application of the artificial neural network in quantitative structure-gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives. Journal of Pharmaceutical and Biomedical Analysis, 28 (3-4). pp. 581-590.

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Abstract

Quantitative structure–retention relationship(QSRR) method was used to model reversed-phase high-performance liquid chromatography (RP-HPLC) separation of 18 selected amino acids. Retention data for phenylthiocarbamyl (PTC) amino acids derivatives were obtained using gradient elution on ODS column with mobile phase of varying acetonitrile, acetate buffer and containing 0.5 ml/l of triethylamine (TEA). Molecular structure of each amino acid was encoded with 36 calculated molecular descriptors. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the genetic neural network method. A genetic algorithm (GA) was used to select important molecular descriptors and supervised artificial neural network (ANN) was used to correlate mobile phase composition and selected descriptors with the experimentally derived retention times. Retention time values were used as the network's output and calculated molecular descriptors and mobile phase composition as the inputs. The best model with five input descriptors was chosen, and the significance of the selected descriptors for amino acid separation was examined. Results confirmed the dominant role of the organic modifier in such chromatographic systems in addition to lipophilicity (log P) and molecular size and shape (topological indices) of investigated solutes.

Item ID:	4718
Item Type:	Article (Research - C1)
ISSN:	1873-264X
Keywords:	amino acids; phenylthiocarbamyl derivatives; artificial neural networks; quantitative structure–retention relationship
Date Deposited:	11 Sep 2009 01:53
FoR Codes:	11 MEDICAL AND HEALTH SCIENCES > 1115 Pharmacology and Pharmaceutical Sciences @ 75% 11 MEDICAL AND HEALTH SCIENCES > 1104 Complementary and Alternative Medicine @ 25%
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