Classification of Plasmodium falciparum glucose-6-phosphate dehydrogenase inhibitors by support vector machine.

ABSTRACT

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.