Research Papers:

Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function

Li Zhang, Hai-Xin Ai, Shi-Meng Li, Meng-Yuan Qi, Jian Zhao, Qi Zhao and Hong-Sheng Liu _

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Oncotarget. 2017; 8:83142-83154. https://doi.org/10.18632/oncotarget.20915

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Li Zhang1,2,*, Hai-Xin Ai1,2,3,*, Shi-Meng Li1, Meng-Yuan Qi1, Jian Zhao1, Qi Zhao4 and Hong-Sheng Liu1,2,3

1School of Life Science, Liaoning University, Shenyang 110036, China

2Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang 110036, China

3Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang 110036, China

4School of Mathematics, Liaoning University, Shenyang 110036, China

*The first two authors should be regarded as joint first authors

Correspondence to:

Hong-Sheng Liu, email: [email protected]

Keywords: influenza virus, neuraminidase inhibitor, virtual screening, machine learning, scoring function

Received: August 02, 2017    Accepted: August 28, 2017    Published: September 15, 2017


In recent years, an epidemic of the highly pathogenic avian influenza H7N9 virus has persisted in China, with a high mortality rate. To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase (NA) protein. RF-NA-Score is more accurate than RF-Score, with a root-mean-square error of 1.46, Pearson’s correlation coefficient of 0.707, and Spearman’s rank correlation coefficient of 0.707 in a 5-fold cross-validation study. The performance of RF-NA-Score in a docking-based virtual screening of NA inhibitors was evaluated with a dataset containing 281 NA inhibitors and 322 noninhibitors. Compared with other docking–rescoring virtual screening strategies, rescoring with RF-NA-Score significantly improved the efficiency of virtual screening, and a strategy that averaged the scores given by RF-NA-Score, based on the binding conformations predicted with AutoDock, AutoDock Vina, and LeDock, was shown to be the best strategy. This strategy was then applied to the virtual screening of NA inhibitors in the SPECS database. The 100 selected compounds were tested in an in vitro H7N9 NA inhibition assay, and two compounds with novel scaffolds showed moderate inhibitory activities. These results indicate that RF-NA-Score improves the efficiency of virtual screening for NA inhibitors, and can be used successfully to identify new NA inhibitor scaffolds. Scoring functions specific for other drug targets could also be established with the same method.

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