Quantitative Structure-Activity Relationship Modeling of the Antioxidant Activity of Some Plant Compounds on Graph of Molecular Descriptors
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Plants synthesize antioxidant compounds as a defense mechanism against reactive oxygen species. Recently, plant-derived antioxidantcompounds have attracted attention due to the increasing consumer awareness in the heath industry. However, traditional methods formeasuring the antioxidant activity of these compounds are time-consuming and costly. Therefore, our study constructed a quantitativestructure-activity relationship (QSAR) model that can predict antioxidant activity using graph convolutional networks (GCN) from plantstructural data. The accuracy (Acc) of the model reached 0.6 and the loss reached 0.03. Although with lower accuracy than previouslyreported QSAR models, our model showed the possibility of predicting DPPH antioxidant activity in a wide range of plant compounds(phenolics, polyphenols, vitamins, etc.) based on their graph structure.
Abstract
Plants synthesize antioxidant compounds as a defense mechanism against reactive oxygen species. Recently, plant-derived antioxidantcompounds have attracted attention due to the increasing consumer awareness in the heath industry. However, traditional methods formeasuring the antioxidant activity of these compounds are time-consuming and costly. Therefore, our study constructed a quantitativestructure-activity relationship (QSAR) model that can predict antioxidant activity using graph convolutional networks (GCN) from plantstructural data. The accuracy (Acc) of the model reached 0.6 and the loss reached 0.03. Although with lower accuracy than previouslyreported QSAR models, our model showed the possibility of predicting DPPH antioxidant activity in a wide range of plant compounds(phenolics, polyphenols, vitamins, etc.) based on their graph structure.
참고문헌 (22)
- Ahmadi S, "Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals", J. Mol. Struct, 1191, pp.165~174 (2019)
- Bergstra J, "Random search for hyper-parameter optimization", J. Mach Learn Res, 13(2) (2012)
- Deng W, "AODB : A comprehensive database for antioxidants including small molecules, peptides and proteins", Food Chem, 418, pp.135992~ (2023)
- Dimitrios B, "Sources of natural phenolic antioxidants", Trends Food Sci. Technol, 17(9), pp.505~512 (2006)
- Djeradi H, "Antioxidant activity of flavonoids : A QSAR modeling using fukui indices descriptors", J. Mol. Model, 20, pp.1~9 (2014)
- Duchowicz PR, "QSAR studies of the antioxidant activity of anthocyanins", J. Food Sci. Technol, 56, pp.5518~5530 (2019)
- Gurib-Fakim A, "Medicinal plants : Traditions of yesterday and drugs of tomorrow", Mol Aspects Med, 27(1), pp.1~93 (2006)
- Kasote DM, "Significance of antioxidant potential of plants and its relevance to therapeutic applications", Int. J. Biol. Sci, 11(8), pp.982~991 (2015)
- Khlebnikov AI, "Improved quantitative structure-activity relationship models to predict antioxidant activity of flavonoids in chemical, enzymatic, and cellular systems", Bioorg Med. Chem, 15(4), pp.1749~1770 (2007)
- Landrum G, "Rdkit documentation", Release, 1(1-79), pp.4~ (2013)
- Meier BP, "The influence of safety, efficacy, and medical condition severity on natural versus synthetic drug preference", Med. Decis. Making, 36(8), pp.1011~1019 (2016)
- Muratov EN, "QSAR without borders", Chem. Soc. Rev, 49(11), pp.3525~3564 (2020)
- Rastija V, "QSAR study of antioxidant activity of wine polyphenols", Eur. J. of Med. Chem, 44(1), pp.400~408 (2009)
- Rasulev B, "A quantitative structure-activity relationship(QSAR)study of the antioxidant activity of flavonoids", QSAR Comb. Sci, 24(9), pp.1056~1065 (2005)
- Seyidoglu N, "Liana Claudia Salanță", pp.7~ (2020), The Health Benefits of Foods
- Shi Y, "Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds", Sci. Rep, 11(1), pp.8806~ (2021)
- Spiegel M, "Antioxidant activity of selected phenolic Acids-Ferric reducing antioxidant power assay and QSAR analysis of the structural features", Molecules, 25(13) (2020)
- Sytar O, "Comparative analysis of bioactive phenolic compounds composition from 26 medicinal plants", Saudi J. Biol. Sci, 25(4), pp.631~641 (2018)
- Tropsha A, "The importance of being earnest : Validation is the absolute essential for successful application and interpretation of QSPR models", QSAR Comb. Sci, 22(1), pp.69~77 (2003)