Analytical Chemistry and Pharmaceutical Technology, Center for Pharmaceutical Research, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium.
Anal Chim Acta. 2011 Oct 31;705(1-2):98-110. doi: 10.1016/j.aca.2011.04.019. Epub 2011 Apr 20.
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.
本文描述了构建一个定量构效关系（QSAR）模型，以将新隐丹参酮的各种衍生物的结构与其抗疟活性相关联。QSAR 分类模型使用线性判别分析（LDA）、二次判别分析（QDA）、分类和回归树（CART）、偏最小二乘判别分析（PLS-DA）、正交投影到潜在结构判别分析（OPLS-DA）和支持向量机分类（SVM-C）构建，使用四组分子描述符作为解释变量。在分类之前，使用双工算法将分子分为训练集和测试集。比较了不同的分类模型，以评估其预测能力、简单性和可解释性。构建了二进制和多类分类模型。对于分为三类的情况，CART 和 One-Against-One（OAO）-SVM-C 被发现是最具预测能力的方法，而对于分为两类的情况，LDA、QDA 和 CART 是。
