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 language | English (US) |
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Pages (from-to) | 98-110 |
Number of pages | 13 |
Journal | Analytica Chimica Acta |
Volume | 705 |
Issue number | 1-2 |
DOIs | |
State | Published - 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