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

par Dejaegher, Bieke ;Dhooghe, L.;Goodarzi, M;Apers, Sandra;Pieters, Luc;Vander Heyden, Yvan
Référence Analytica chimica acta, 705, 1-2, page (98-110)
Publication Publié, 2011

Article révisé par les pairs

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Résumé :

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.