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Predicting Biological Activity of 2,4,6-trisubstituted 1,3,5-triazines Using Random Forest

  • Ahmed H. Abu El-Atta
  • M. I. Moussa
  • Aboul Ella Hassanien
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)

Abstract

This paper presents an approach to predict the activity of analogues of 2,4,6-trisubstituted 1,3,5-triazines as cannabinoid receptor (CB2) agonists using random forest technique. We compute twenty molecular descriptors for a data set of 58 analogues for the component, and depending on values of these descriptors we train random forest to find a relation between biological activity and molecular structure of analogues. The results obtained by random forest were compared with the decision tree and support vector machine classifiers and the random forest has 100% overall predicting accuracy and for decision tree and support vector machine were 93% and 67% respectively.

Keywords

QSAR model CB2 agonists Decision tree Random forest and Molecular descriptors 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ahmed H. Abu El-Atta
    • 1
    • 2
  • M. I. Moussa
    • 2
  • Aboul Ella Hassanien
    • 1
    • 3
  1. 1.Scientific Research Group in Egypt (SRGE), egyptscience.netCairoEgypt
  2. 2.Faculty of Computers and InformationBenha UniversityBenhaEgypt
  3. 3.Faculty of Computers and InformationCairo UniversityCairoEgypt

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