ArticleMolecular diversity2015
Predicting dual-targeting anti-influenza agents using multi-models.
Article in Molecular diversity, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Asprellcosides B ofFrontiers in microbiology · 2018Article
- Design and one-pot synthesis of 2-thiazolylhydrazone derivatives as influenza neuraminidase inhibitors.Molecular diversity · 2017Article
- Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models.Molecular diversity · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Influenza is an acute respiratory infectious disease caused by influenza viruses. Its subtype can be distinguished based on the antigenicity of two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). One of the main challenges in anti-influenza drug development is the quick evolution of drug resistance due to virus mutations. One solution to this problem is to develop dual-targeting anti-influenza agents. In this paper, a new rationally designed virtual screening protocol that combines structure-based approaches (molecular docking and molecular dynamic simulations) and ligand-based approaches (support vector machines and 3D shape & electrostatic similarity algorithms) is reported for the virtual screening of dual-targeting agents against HA and NA. The final hits came from the consensus of the ligand- and receptor-based knowledge of HA and NA and were tested using ADMET predictions. Evidence from the binding energy calculations and binding mode analyses suggested that several of the hits are promising as dual-targeting anti-influenza agents. The virtual screening protocol may also lead to the identification of innovative drugs in other fields.
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Registered trials
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