Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

@article{uvela2015MolecularDS,
  title={Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.},
  author={Petar Žuvela and J. Jay Liu and Katarzyna Macur and Tomasz Bączek},
  journal={Analytical chemistry},
  year={2015},
  volume={87 19},
  pages={9876-83}
}
In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected… CONTINUE READING
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