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

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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 \(\sigma \) atom charge, \(\pi \) atom charge, and lone pair electronegativity-related descriptors are important for the interaction between the PfG6PD and the inhibitor.

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Acknowledgments

This study was supported by the National Natural Science Foundation of China (20975011) and “Chemical Grid Project” of Beijing University of Chemical Technology. We thank Molecular Networks GmbH, Erlangen, Germany for making the programs ADRIANA.Code, Corina, and SONNIA available for our work.

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  1. 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

    Xiaoli Hou & Aixia Yan

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  1. Xiaoli Hou
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  2. Aixia Yan
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Correspondence to Aixia Yan.

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Hou, X., Yan, A. Classification of Plasmodium falciparum glucose-6-phosphate dehydrogenase inhibitors by support vector machine. Mol Divers 17, 489–497 (2013). https://doi.org/10.1007/s11030-013-9447-9

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