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Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches

Mason Eye Institute, University of Missouri School of Medicine, Columbia, MO 65201, USA
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Int. J. Mol. Sci. 2020, 21(4), 1523; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21041523
Received: 9 January 2020 / Revised: 18 February 2020 / Accepted: 21 February 2020 / Published: 23 February 2020
(This article belongs to the Section Molecular Informatics)
Glucose-6-Phosphate Dehydrogenase (G6PD) is a ubiquitous cytoplasmic enzyme converting glucose-6-phosphate into 6-phosphogluconate in the pentose phosphate pathway (PPP). The G6PD deficiency renders the inability to regenerate glutathione due to lack of Nicotine Adenosine Dinucleotide Phosphate (NADPH) and produces stress conditions that can cause oxidative injury to photoreceptors, retinal cells, and blood barrier function. In this study, we constructed pharmacophore-based models based on the complex of G6PD with compound AG1 (G6PD activator) followed by virtual screening. Fifty-three hit molecules were mapped with core pharmacophore features. We performed molecular descriptor calculation, clustering, and principal component analysis (PCA) to pharmacophore hit molecules and further applied statistical machine learning methods. Optimal performance of pharmacophore modeling and machine learning approaches classified the 53 hits as drug-like (18) and nondrug-like (35) compounds. The drug-like compounds further evaluated our established cheminformatics pipeline (molecular docking and in silico ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis). Finally, five lead molecules with different scaffolds were selected by binding energies and in silico ADMET properties. This study proposes that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability to find potential G6PD activators used for G6PD deficiency diseases. Moreover, these compounds can be considered as safe agents for further validation studies at the cell level, animal model, and even clinic setting. View Full-Text
Keywords: G6PD; pharmacophore modeling; machine learning; docking; ADMET G6PD; pharmacophore modeling; machine learning; docking; ADMET
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MDPI and ACS Style

Saddala, M.S.; Lennikov, A.; Huang, H. Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches. Int. J. Mol. Sci. 2020, 21, 1523. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21041523

AMA Style

Saddala MS, Lennikov A, Huang H. Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches. International Journal of Molecular Sciences. 2020; 21(4):1523. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21041523

Chicago/Turabian Style

Saddala, Madhu S., Anton Lennikov, and Hu Huang. 2020. "Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches" International Journal of Molecular Sciences 21, no. 4: 1523. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21041523

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