The STUDIA UNIVERSITATIS BABEŞ-BOLYAI issue article summary

The summary of the selected article appears at the bottom of the page. In order to get back to the contents of the issue this article belongs to you have to access the link from the title. In order to see all the articles of the archive which have as author/co-author one of the authors mentioned below, you have to access the link from the author's name.

  
       
         
    STUDIA CHEMIA - Issue no. 2,%20Tom%20I / 2019  
         
  Article:   MODELING AND PREDICTION OF AMINO ACIDS LIPOPHYLICITY USING MULTIPLE LINEAR REGRESSION COUPLED WITH GENETIC ALGORITHM.

Authors:  ALEXANDRINA GUIDEA, COSTEL SÂRBU. 		 
       
         
  Abstract:  Quantitative structure-retention relationships (QSRR) approach was used to model chromatographic lipophilicity of sixteen proteinogenic amino acids using molecular descriptors computed with DRAGON and ALCHEMY software packages. Modeling was performed applying multiple linear regression (MLR) coupled with genetic algorithms (GA) methodology (MLR-GA). The most important descriptors, highly significant in the predictive models of amino acids lipophilicity (RM0), were related to atomic polarizabilities (MATS3p; Ap; H1p), atomic van der Waals volume (MATS3v), Sanderson electronegativity (RDF070e) and Randic molecular profiles (DP11; DP12) calculated with Dragon software. The internal statistical evaluation procedure highlighted some appropriate models for the chromatographic lipophilicity prediction. Moreover, the statistical parameters of regression in order to evaluate the relationship between experimental and predicted values, in case of the test set (four amino acids), revealed three statistically valid models (model A, E and F) that can be successfully used in lipophilicity prediction of amino acids.

Keywords: chromatographic lipophilicity, amino acids, multiple linear regression, genetic algorithm, molecular descriptors, modeling, prediction 		 
         
     
         
         
      Back to previous page