ArticleOncotarget2017
Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed, 57 citations in OpenAlex.
- Research Progress and Screening Strategies of Natural Product-Derived Neuraminidase Inhibitors.Biosensors · 2026Review
- Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1.Molecular diversity · 2025Article
- UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines.Journal of cheminformatics · 2025Article
- Building shape-focused pharmacophore models for effective docking screening.Journal of cheminformatics · 2024Article
- Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 MJournal of computer-aided molecular design · 2024Article
- Antiviral Approaches against Influenza Virus.Clinical microbiology reviews · 2023Review
- Improved machine learning scoring functions for identification of Electrophorus electricus's acetylcholinesterase inhibitors.Molecular diversity · 2022Article
- Designing Novel Compounds for the Treatment and Management of RET-Positive Non-Small Cell Lung Cancer-Fragment Based Drug Design Strategy.Molecules (Basel, Switzerland) · 2022Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Disease-Ligand Identification Based on Flexible Neural Tree.Frontiers in microbiology · 2022Article
- Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review.Robotics and autonomous systems · 2021Review
- Potential drugs against COVID-19 revealed by gene expression profile, molecular docking and molecular dynamic simulation.Future virology · 2021Article
- Potent sialic acid inhibitors that target influenza A virus hemagglutinin.Scientific reports · 2021Article
- Recent advances on the machine learning methods in predicting ncRNA-protein interactions.Molecular genetics and genomics : MGG · 2021Review
- Molecular dynamics simulation of docking structures of SARS-CoV-2 main protease and HIV protease inhibitors.Journal of molecular structure · 2021Article
- Predicting New Anti-Norovirus Inhibitor With the Help of Machine Learning Algorithms and Molecular Dynamics Simulation-Based Model.Frontiers in chemistry · 2021Article
- A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19.BioMed research international · 2021Review
- The role of human in the loop: lessons from D3R challenge 4.Journal of computer-aided molecular design · 2020Article
- Tailoring Proteins to Re-Evolve Nature: A Short Review.Molecular biotechnology · 2018Review
- Inhibition of sialidase activity as a therapeutic approach.Drug design, development and therapy · 2018Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.