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

• Published in: Analytica chimica acta Vol. 705; no. 1; pp. 98 - 110
• Main Authors: Dejaegher, B., Dhooghe, L., Goodarzi, M., Apers, S., Pieters, L., Vander Heyden, Y.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 31.10.2011; Elsevier
• Subjects: Alkaloids - chemistry; Alkaloids - pharmacology; Analytical chemistry; Antimalarials - chemistry; Antimalarials - pharmacology; Chemistry; Classification and Regression Trees; Classification models; Discriminant Analysis; Exact sciences and technology; Hemeproteins - antagonists & inhibitors; Hemeproteins - metabolism; Humans; Least-Squares Analysis; Linear Discriminant Analysis; Malaria - drug therapy; Models, Biological; Orthogonal Projection to Latent Structures – Discriminant Analysis; Partial Least Squares – Discriminant Analysis; Plasmodium falciparum - drug effects; Quadratic Discriminant Analysis; Quantitative Structure-Activity Relationship; Quinolines - chemistry; Quinolines - pharmacology; Support Vector Machine; Support Vector Machines for Classification; β-Haematin inhibition; Partial Least Squares – Discriminant Analysis; Orthogonal Projection to Latent Structures – Discriminant Analysis; Quadratic Discriminant Analysis; Linear Discriminant Analysis; Classification and Regression Trees; β-Haematin inhibition; Support Vector Machines for Classification; Classification models; Discriminant analysis; Construction; Support; Method; Algorithm; P-Haematin inhibition; Molecules; PLS regression; Structure activity relation; Analysis; Classification; Partial Least Squares - Discriminant; Inhibitor; Inhibition; Orthogonal Projection to Latent Structures; Vector
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2011.04.019

[Display omitted] ► Classification of various neocryptolepine derivatives according to their anti-malarial activity. ► Use of LDA, QDA, CART, PLS-DA, OPLS-DA, OAO-SVM-C, and OAA-SVM-C for classification. ► CART model preferred for three-class classification according to activity. ► LDA and QDA models preferred for two-class classification according to activity. 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.