QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm-Reference-Cited by-同舟云学术

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QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm

Published:2020-09-17 Issue:11 Volume:31 Page:803-814
ISSN:1062-936X
Container-title:SAR and QSAR in Environmental Research
language:en
Short-container-title:SAR and QSAR in Environmental Research

Author:

Algamal Z.Y.¹^ORCID,Qasim M.K.²,Lee M.H.³^ORCID,Ali H.T.M.⁴

Affiliation:

1. Department of Statistics and Informatics, University of Mosul, Mosul, Iraq

2. Department of General Science, University of Mosul, Mosul, Iraq

3. Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia

4. College of Computers and Information Technology, Nawroz University, Dahuk, Iraq

Publisher

Informa UK Limited

Subject

Drug Discovery,Molecular Medicine,General Medicine,Bioengineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/1062936X.2020.1818616

Reference38 articles.

1. Quantitative structure–activity relationship study of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors by genetic algorithm feature selection and support vector regression

2. Influenza Neuraminidase Inhibitors Possessing a Novel Hydrophobic Interaction in the Enzyme Active Site: Design, Synthesis, and Structural Analysis of Carbocyclic Sialic Acid Analogues with Potent Anti-Influenza Activity

3. Syntheses and Neuraminidase Inhibitory Activity of Multisubstituted Cyclopentane Amide Derivatives

4. C-Methylated Flavonoids from Cleistocalyx operculatus and Their Inhibitory Effects on Novel Influenza A (H1N1) Neuraminidase

5. Antiviral Potential and Molecular Insight into Neuraminidase Inhibiting Diarylheptanoids from Alpinia katsumadai

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