title: [The relationship between the thin-layer chromatographic retention values and the molecular structures of a group of amino acids by using back-propagation artificial neural networks].
abstract: The relationship between the thin-layer chromatographic retention values and the molecular structures of fifteen amino acids was studied by using back-propagation artificial neural networks (ANNs). In this paper, firstly, lots of parameters of amino acids have been determined, accumulated and computed. Then, correlation coefficients of all parameters were computed by taking advantage of correlation analysis. Taking the correlation coefficient approaching one as the criteria in the correlation analysis, all parameters were classified into three kinds. Three parameters were selected from each kind, respectively, to consist of one group. Then, the optimized groups of parameters, which have clearer physicochemical meanings, were used as the inputting parameters of artificial neural networks. Correlation coefficients of experimental retardation values and those calculated by using ANNs were computed and showed good agreement. The present work shows that the ANNs method may take an important role in the study of the relationship between TLC behavior and compound structure.
authors.affiliation: Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116012.
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: J
authors.initials: J
authors.lastname: Zhang
authors.forename: L
authors.initials: L
authors.lastname: Lin
mesh_terms: D000596:Amino Acids / Q000032:analysis / Q000737:chemistry*; D002855:Chromatography, Thin Layer / Q000379:methods*; D001330:Electronic Data Processing; D015394:Molecular Structure; D016571:Neural Networks, Computer
chemical_list: D000596:Amino Acids