RESUMO
Quantitative structure-property relationship (QSPR) method was performed for the prediction of the standard Gibbs energies (DeltaGtheta) of the transfer of peptide anions from aqueous solution to nitrobenzene. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the peptides. The four molecular descriptors selected by the heuristic method (HM) in COmprehensive DEscriptors for Structural and Statistical Analysis (CODESSA) were used as inputs for support vector machine (SVM) and radial basis function neural networks (RNFNN). The results obtained by the novel machine learning technique, SVM, were compared with those obtained by HM and RBFNN. The root mean squared errors (RMS) of the training, predicted and overall data sets are 2.192, 2.541 and 2.267 unit (kJ/mol) for HM, 1.604, 2.478 and 1.817 unit (kJ/mol) for RBFNN and 1.5621, 2.364 and 1.756 unit (kJ/mol) for SVM, respectively. The prediction results were in agreement with the experimental values. This paper provided a potential method for predicting the physiochemical property (DeltaGtheta) of various small peptides.
Assuntos
Ânions/química , Modelos Químicos , Nitrobenzenos/química , Peptídeos/química , Solventes/química , Água/química , Relação Estrutura-Atividade , TermodinâmicaRESUMO
A method for the determination of cinoxacin and nalidixic acid in human plasma by reverse-phase high performance liquid chromatography is introduced. The single separation system involves a RP-C18 column, a ultraviolet detector at 254 mn and CH3CN-CH3OH-0.01 mol/L HOOCOOH (30:5:65;pH 4.55). The influence of two kind of buffer (phosphate buffer and oxalate buffer) in mobile phase on the separation were studied. Oxalate buffer can improve tailing peak and decrease detection limits. Three pH values (pH 2.80,4.08,4.55) were studied, pH 4.55 can give best separation result. The detection limits of cinoxacin and nalidixic acid were 0.14 ng and 0.24 ng, respectively.
