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Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning

Department of Chemistry and Life Science, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan
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Academic Editor: Riccardo Amorati
Antioxidants 2021, 10(11), 1751; https://doi.org/10.3390/antiox10111751
Received: 11 October 2021 / Revised: 28 October 2021 / Accepted: 28 October 2021 / Published: 1 November 2021
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. View Full-Text
Keywords: machine learning; antioxidant; singlet oxygen; feature importance; interpretability; carotenoid machine learning; antioxidant; singlet oxygen; feature importance; interpretability; carotenoid
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MDPI and ACS Style

Fujimoto, T.; Gotoh, H. Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning. Antioxidants 2021, 10, 1751. https://doi.org/10.3390/antiox10111751

AMA Style

Fujimoto T, Gotoh H. Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning. Antioxidants. 2021; 10(11):1751. https://doi.org/10.3390/antiox10111751

Chicago/Turabian Style

Fujimoto, Taiki, and Hiroaki Gotoh. 2021. "Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning" Antioxidants 10, no. 11: 1751. https://doi.org/10.3390/antiox10111751

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