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Medicinal Chemistry Research

, Volume 21, Issue 9, pp 2680–2688 | Cite as

Application of GA–KPLS and L–M ANN calculations for the prediction of the capacity factor of hazardous psychoactive designer drugs

  • Hadi NoorizadehEmail author
  • Abbas Farmany
  • Mehrab Noorizadeh
Original Research

Abstract

The hazardous psychoactive designer drugs are compounds in which part of the molecular structure of a stimulant or narcotic has been modified. Genetic algorithm and kernel partial least square (GA–KPLS) and Levenberg–Marquardt artificial neural network (L–M ANN) techniques were used to investigate the correlation between capacity factor (k′) and descriptors for 104 hazardous psychoactive designer drugs. These drugs are containing Tryptamine, Phenylethylamine, and Piperazine. The both methods resulted in accurate prediction whereas more accurate results were obtained by L–M ANN model. The best model obtained from L–M ANN showed a good R 2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to GA–KPLS models. The stability and prediction ability of these models were validated using leave-group-out cross-validation, external test set, and Y-randomization techniques. This is the first research on the quantitative structure–retention relationship (QSRR) of the designer drugs using the GA–KPLS and L–M ANN.

Keywords

Hazardous psychoactive designer drugs Tryptamine Phenylethylamine Piperazine QSRR Genetic algorithm–kernel partial least square Levenberg–Marquardt artificial neural network 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Hadi Noorizadeh
    • 1
    Email author
  • Abbas Farmany
    • 1
  • Mehrab Noorizadeh
    • 2
  1. 1.Department of Chemistry, Faculty of Sciences, Ilam BranchIslamic Azad UniversityIlamIran
  2. 2.Young Researchers Club, Ilam BranchIslamic Azad UniversityIlamIran

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