In silico prediction models for thyroid peroxidase inhibitors and their application to synthetic flavors

Mihyun Seo; Changwon Lim; Hoonjeong Kwon

doi:10.1007/s10068-022-01041-y

In silico prediction models for thyroid peroxidase inhibitors and their application to synthetic flavors

Food Sci Biotechnol. 2022 Mar 12;31(4):483-495. doi: 10.1007/s10068-022-01041-y. eCollection 2022 Apr.

Authors

Mihyun Seo¹, Changwon Lim², Hoonjeong Kwon^{1

3}

Affiliations

¹ Department of Food and Nutrition, Seoul National University, Seoul, Republic of Korea.
² Department of Applied Statistics, Chung-Ang University, Seoul, Republic of Korea.
³ Research Institute of Human Ecology, Seoul National University, Seoul, Republic of Korea.

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s10068-022-01041-y.

Keywords: Machine learning; Quantitative structure–activity relationship (QSAR); Synthetic flavor; Thyroid peroxidase inhibitor (TPO); Toxicity prediction.