ArticleAntioxidants (Basel, Switzerland)2021
Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning.
Article in Antioxidants (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Leakage-Aware Machine Learning and Deep Learning Benchmarking of Food Antioxidant Capacity Prediction on the Antioxidant Food Table.Antioxidants (Basel, Switzerland) · 2026Article
- DPPH Measurement for Phenols and Prediction of Antioxidant Activity of Phenolic Compounds in Food.Current issues in molecular biology · 2025Article
- Kinetic and thermodynamic evaluation of antioxidant reactions: factors influencing the radical scavenging properties of phenolic compounds in foods.Journal of the science of food and agriculture · 2025Article
- Predicting the Toxicity of Drug Molecules with Selecting Effective Descriptors Using a Binary Ant Colony Optimization (BACO) Feature Selection Approach.Molecules (Basel, Switzerland) · 2025Article
- DPPH Measurements and Structure-Activity Relationship Studies on the Antioxidant Capacity of Phenols.Antioxidants (Basel, Switzerland) · 2024Article
- Low-cost machine learning prediction of excited state properties of iridium-centered phosphors.Chemical science · 2023Article
- Feature Selection for the Interpretation of Antioxidant Mechanisms in Plant Phenolics.Molecules (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A chemically explainable machine learning model was constructed with a small dataset to quantitatively predict the singlet-oxygen-scavenging ability. In this model, ensemble learning based on decision trees resulted in high accuracy. For explanatory variables, molecular descriptors by computational chemistry and Morgan fingerprints were used for achieving high accuracy and simple prediction. The singlet-oxygen-scavenging mechanism was explained by the feature importance obtained from machine learning outputs. The results are consistent with conventional chemical knowledge. The use of machine learning and reduction in the number of measurements for screening high-antioxidant-capacity compounds can considerably improve prediction accuracy and efficiency.
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Registered trials
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