Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6-Reference-Cited by-同舟云学术

Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6

Published:2024-03-07 Issue:6 Volume:64 Page:1996-2007
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zdybel Szymon¹²,Sosnowska Anita¹²^ORCID,Kowalska Dominika¹,Sommer Julia³,Conrady Beate⁴,Mester Patrick³^ORCID,Gromelski Maciej¹,Puzyn Tomasz¹²^ORCID

Affiliation:

1. QSAR Lab, ul. Trzy Lipy 3, 80-172 Gdańsk, Poland

2. Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdańsk, Poland

3. Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 Vienna, Austria

4. Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 8, 1870 Frederiksberg Campus, Copenhagen DK-1870, Denmark

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c02037

Reference52 articles.

1. Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

2. MODERNIZING TOXICITY TESTS

3. Virucidal or Not Virucidal? That Is the Question—Predictability of Ionic Liquid’s Virucidal Potential in Biological Test Systems

4. Predictability of ionic liquid toxicity from a SAR study on different systematic levels of pathogenic bacteria

5. Antibacterial activity of Ionic Liquids based on ampicillin against resistant bacteria