ArticleMolecular diversity2016
Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models.
Article in Molecular diversity, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 29 citations in OpenAlex.
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Discovering Influenza Virus Neuraminidase Inhibitors via Computational and Experimental Studies.ACS omega · 2024Article
- Development and characterization of an antibody that recognizes influenza virus N1 neuraminidases.PloS one · 2024Article
- QSPRmodeler - An open source application for molecular predictive analytics.Frontiers in bioinformatics · 2024Article
- Antiviral Approaches against Influenza Virus.Clinical microbiology reviews · 2023Review
- Identification of a Family of Glycoside Derivatives Biologically Active againstPharmaceuticals (Basel, Switzerland) · 2023Article
- Discovery of Multitarget-Directed Ligands Against Influenza A Virus From Compound Yizhihao Through a Predictive System for Compound-Protein Interactions.Frontiers in cellular and infection microbiology · 2020Article
- Discovery of VEGFR2 inhibitors by integrating naïve Bayesian classification, molecular docking and drug screening approaches.RSC advances · 2018Article
- QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.Frontiers in pharmacology · 2018Article
- Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.Current pharmaceutical design · 2018Review
- Design and one-pot synthesis of 2-thiazolylhydrazone derivatives as influenza neuraminidase inhibitors.Molecular diversity · 2017Article
- DL0410 Ameliorates Memory and Cognitive Impairments Induced by Scopolamine via Increasing Cholinergic Neurotransmission in Mice.Molecules (Basel, Switzerland) · 2017Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibitors. In this work, we built support vector machine and Naïve Bayesian models of NA inhibitors and non-inhibitors, with different ratios of active-to-inactive compounds in the training set and different molecular descriptors. Four models with sensitivity or Matthews correlation coefficients greater than 0.9 were chosen to predict the NA inhibitory activities of 15,600 compounds in our in-house database. We combined the results of four optimal models and selected 60 representative compounds to assess their NA inhibitory profiles in vitro. Nine NA inhibitors were identified, five of which were oseltamivir derivatives with large C-5 substituents exhibiting potent inhibition against H1N1 NA with IC50 values in the range of 12.9-185.0 nM, and against H3N2 NA with IC50 values between 18.9 and 366.1 nM. The other four active compounds belonged to novel scaffolds, with IC50 values ranging 39.5-63.8 μM against H1N1 NA and 44.5-114.1 μM against H3N2 NA. This is the first time that classification models of NA inhibitors and non-inhibitors are built and their prediction results validated experimentally using in vitro assays.
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