In silico prediction models for thyroid peroxidase inhibitors and their application to synthetic flavorsopen accessIn silico prediction models for thyroid peroxidase inhibitors and their application to synthetic flavors
- Authors
- Seo, M.; Lim, Changwon; Kwon, H.
- Issue Date
- Apr-2022
- Publisher
- The Korean Society of Food Science and Technology
- Keywords
- Machine learning; Quantitative structure–activity relationship (QSAR); Synthetic flavor; Thyroid peroxidase inhibitor (TPO); Toxicity prediction
- Citation
- Food Science and Biotechnology, v.31, no.4, pp 483 - 495
- Pages
- 13
- Journal Title
- Food Science and Biotechnology
- Volume
- 31
- Number
- 4
- Start Page
- 483
- End Page
- 495
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/55664
- DOI
- 10.1007/s10068-022-01041-y
- ISSN
- 1226-7708
2092-6456
- Abstract
- Systematic toxicity tests are often waived for the synthetic flavors as they are added in a very small amount in foods. However, their safety for some endpoints such as endocrine disruption should be concerned as they are likely to be active in low levels. In this case, structure–activity-relationship (SAR) models are good alternatives. In this study, therefore, binary, ternary, and quaternary prediction models were designed using simple or complex machine-learning methods. Overall, hard-voting classifiers outperformed other methods. The test scores for the best binary, ternary, and quaternary models were 0.6635, 0.5083, and 0.5217, respectively. Along with model development, some substructures including primary aromatic amine, (enol)ether, phenol, heterocyclic sulfur, and heterocyclic nitrogen, dominantly occurred in the most highly active compounds. The best predicting models were applied to synthetic flavors, and 22 agents appeared to have a strong inhibitory potential towards TPO activities. © 2022, The Author(s).
- Files in This Item
-
- Appears in
Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/55664)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.