کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1166679 | 1491126 | 2011 | 13 صفحه PDF | دانلود رایگان |
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.
Figure optionsDownload as PowerPoint slideHighlights
► 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.
Journal: Analytica Chimica Acta - Volume 705, Issues 1–2, 31 October 2011, Pages 98–110