Stochastic-based descriptors studying peptides biological properties: modeling the bitter tasting threshold of dipeptides.

@article{Armas2004StochasticbasedDS,
  title={Stochastic-based descriptors studying peptides biological properties: modeling the bitter tasting threshold of dipeptides.},
  author={Ronal Ramos de Armas and Humberto Gonz{\'a}lez D{\'i}az and Reinaldo Molina and Maykel P{\'e}rez Gonz{\'a}lez and Eugenio Uriarte},
  journal={Bioorganic & medicinal chemistry},
  year={2004},
  volume={12 18},
  pages={
          4815-22
        }
}
MARCH-INSIDE methodology was applied to the prediction of the bitter tasting threshold of 48 dipeptides by means of pattern recognition techniques, in this case linear discriminant analysis (LDA), and regression methods. The LDA models yielded a percentage of good classification higher than 80% with the two main families of descriptor generated by this methodology (95.8% for self return probability and 83.3% using electronic delocalization entropy). The regression models can explain more than… CONTINUE READING

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