Antioxidant activity of NSAIDs-Se derivatives: predictive QSAR-machine learning models-Reference-Cited by-同舟云学术

Antioxidant activity of NSAIDs-Se derivatives: predictive QSAR-machine learning models

Published:2024 Issue: Volume: Page:
ISSN:1144-0546
Container-title:New Journal of Chemistry
language:en
Short-container-title:New J. Chem.

Author:

Fu Zhihui¹,Wiriyarattanakul Amphawan²,Xie Wanting¹,Jantorn Pattamon³⁴,Toopradab Borwornlak³⁴,Shi Liyi¹⁵,Rungrotmongkol Thanyada³⁴^ORCID,Maitarad Phornphimon¹^ORCID

Affiliation:

1. Research Center of Nano Science and Technology, College of Sciences, Shanghai University, Shanghai 200444, P. R. China

2. Program in Chemistry, Faculty of Science and Technology, Uttaradit Rajabhat University, Uttaradit 53000, Thailand

3. Center of Excellence in Structural and Computational Biology, Department of Biochemistry, Chulalongkorn University, Bangkok 10330, Thailand

4. Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand

5. Emerging Industries Institute Shanghai University, Jiaxing, Zhejiang 314006, P. R. China

Abstract

This investigation utilized the random forest (RF) and artificial neural network (ANN) techniques within the quantitative structure–activity relationship (QSAR) framework to assess NSAIDs-Se derivatives and their antioxidant properties.

Funder

National Research Council of Thailand

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/NJ/D4NJ03216K

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