State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, P.O. Box 53, Beijing, 100029, China.
Mol Divers. 2013 Aug;17(3):489-97. doi: 10.1007/s11030-013-9447-9. Epub 2013 May 9.
Plasmodium falciparum glucose-6-phosphate dehydrogenase (PfG6PD) has been considered as a potential target for severe forms of anti-malaria therapy. In this study, several classification models were built to distinguish active and weakly active PfG6PD inhibitors by support vector machine method. Each molecule was initially represented by 1,044 molecular descriptors calculated by ADRIANA.Code. Correlation analysis and attribute selection methods in Weka were used to get the best reduced set of molecular descriptors, respectively. The best model (Model 2w) gave a prediction accuracy (Q) of 93.88 % and a Matthew's correlation coefficient (MCC) of 0.88 on the test set. Some properties such as [Formula: see text] atom charge, [Formula: see text] atom charge, and lone pair electronegativity-related descriptors are important for the interaction between the PfG6PD and the inhibitor.
恶性疟原虫葡萄糖-6-磷酸脱氢酶（PfG6PD）被认为是抗疟治疗严重形式的潜在靶点。在这项研究中，使用支持向量机方法构建了几种分类模型，以区分活性和弱活性 PfG6PD 抑制剂。每个分子最初由 ADRIANA.Code 计算的 1044 个分子描述符表示。在 Weka 中使用相关性分析和属性选择方法，分别得到最佳的分子描述符简化集。最佳模型（Model 2w）在测试集上的预测准确率（Q）为 93.88%，马修斯相关系数（MCC）为 0.88。一些性质，如[公式：见文本]原子电荷、[公式：见文本]原子电荷和孤对电负性相关描述符，对于 PfG6PD 与抑制剂之间的相互作用很重要。
