期刊
FOOD CHEMISTRY
卷 372, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2021.131249
关键词
Sweetener; Machine learning; Sweetness; Virtual screening; Molecular cloud; Matched molecular pair analysis
资金
- National Natural Science Foundation of China [22003078]
- Hunan Provincial Natural Science Foundation of China [2019JJ51003]
A novel multi-layer sweetness evaluation system based on machine learning methods was proposed to evaluate sweet properties of compounds with different chemical spaces and categories, providing quantitative predictions of sweetness. The study obtained sweetness-related chemical basis and structure transforming rules using molecular cloud and matched molecular pair analysis (MMPA) methods. The research aims to facilitate food scientists with efficient screening and precise development of high-quality sweeteners.
Nowadays, computational approaches have drawn more and more attention when exploring the relationship between sweetness and chemical structure instead of traditional experimental tests. In this work, we proposed a novel multi-layer sweetness evaluation system based on machine learning methods. It can be used to evaluate sweet properties of compounds with different chemical spaces and categories, including natural, artificial, carbohydrate, non-carbohydrate, nutritive and non-nutritive ones, suitable for different application scenarios. Furthermore, it provided quantitative predictions of sweetness. In addition, sweetness-related chemical basis and structure transforming rules were obtained by using molecular cloud and matched molecular pair analysis (MMPA) methods. This work systematically improved the data quality, explored the best machine learning algorithm and molecular characterizing strategy, and finally obtained robust models to establish a multi-layer prediction system (available at: https://github.com/ifyoungnet/ChemSweet). We hope that this study could facilitate food scientists with efficient screening and precise development of high-quality sweeteners.
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