Classification models for neocryptolepine derivatives as inhibitors of the β-haematin formation

B. Dejaegher, L. Dhooghe, M. Goodarzi, S. Apers, L. Pieters, Y. Vander Heyden

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

This paper describes the construction of a QSAR model to relate the structures of various derivatives of neocryptolepine to their anti-malarial activities. QSAR classification models were build using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Classification and Regression Trees (CART), Partial Least Squares - Discriminant Analysis (PLS-DA), Orthogonal Projections to Latent Structures - Discriminant Analysis (OPLS-DA), and Support Vector Machines for Classification (SVM-C), using four sets of molecular descriptors as explanatory variables. Prior to classification, the molecules were divided into a training and a test set using the duplex algorithm. The different classification models were compared regarding their predictive ability, simplicity, and interpretability. Both binary and multi-class classification models were constructed. For classification into three classes, CART and One-Against-One (OAO)-SVM-C were found to be the best predictive methods, while for classification into two classes, LDA, QDA and CART were.

Original languageEnglish (US)
Pages (from-to)98-110
Number of pages13
JournalAnalytica Chimica Acta
Volume705
Issue number1-2
DOIs
StatePublished - Oct 31 2011

Keywords

  • Classification and Regression Trees
  • Classification models
  • Linear Discriminant Analysis
  • Orthogonal Projection to Latent Structures - Discriminant Analysis
  • Partial Least Squares - Discriminant Analysis
  • Quadratic Discriminant Analysis
  • Support Vector Machines for Classification
  • β-Haematin inhibition

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Environmental Chemistry
  • Spectroscopy

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