Title
Classification models for neocryptolepine derivatives as inhibitors of the <tex>$\beta$</tex>-haematin formation Classification models for neocryptolepine derivatives as inhibitors of the <tex>$\beta$</tex>-haematin formation
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences. Pharmacy
Publication type
article
Publication
Amsterdam ,
Subject
Pharmacology. Therapy
Source (journal)
Analytica chimica acta. - Amsterdam
Volume/pages
705(2011) :1/2 , p. 98-110
ISSN
0003-2670
ISI
000295993900014
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
E-info
https://repository.uantwerpen.be/docman/iruaauth/0a34d7/599a1659453.pdf
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