ArticleJournal of xenobiotics2025
QSAR Models for Predicting the Antioxidant Potential of Chemical Substances.
Article in Journal of xenobiotics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed.
- Towards the Development of Effective Antioxidants-The Molecular Structure and Properties-Part 2.Molecules (Basel, Switzerland) · 2026Review
- Emerging and Versatile Non-Mammalian Model Organisms for Studying the In Vivo Antioxidant Properties of Food-Derived Bioactive Compounds.Antioxidants (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Antioxidants are widely studied compounds with significant applications in the nutraceutical and dietary industries. To enable the rapid screening of large libraries of substances for antioxidant activity and to provide a useful tool for the initial evaluation of substances of interest with unknown activity, we developed Quantitative Structure-Activity Relationship (QSAR) models to predict the antioxidant potential of chemical substances. We started from a dataset of 1911 antioxidant substances, retrieved from the AODB database by selecting the DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging activity assay and the experimental value of the half-maximal inhibitory concentration. Different machine learning algorithms were applied to build regression models, and the goodness-of-fit of each model was assessed using the statistical parameters of R squared (R
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