Predicting pungency and understanding the pungency mechanism of capsaicinoids using TOPS-MODE approach. Issue 7 (2nd July 2020)

• Record Type: Journal Article
• Authors: Yu, S.; Jia, S.; Wang, D.; Lv, Z.; Chen, Y.; Wang, N.; Yao, W.; Yuan, J.
• Is Part Of: SAR and QSAR in environmental research. Volume 31:Issue 7(2020)
• Page Start: 527
• Page End: 545
• Publication Date: 2020-07-02
• Subjects: Quantitative structure–property relationship -- pungency -- capsaicinoids -- topological sub-structural molecular descriptors -- multiple linear regression; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2020.1777583
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: Quantitative structure–property relationship (QSPR) models were developed for predicting the pungency of a set of capsaicinoids. Multiple linear regression (MLR) coupled with topological substructural molecular descriptor (TOPS-MODE) approach was used. The best MLR model based on only five orthogonalized TOPS-MODE variables allowed us to obtain a coefficient of determination of 0.954 on the training set. The predictive power of the model was validated through a test set and several external validation parameters. This showed that the TOPS-MODE descriptors weighted by bond dipole moments, van der Waals atomic radii, and the total solute hydrogen bond basicity affected pungency. The contributions of certain bonds and fragments to pungency were used to understand the pungency mechanism of capsaicinoids. The selected model can more accurately predict pungency of capsaicinoids compared than those found in the literature, and especially bring insights into the structural features and chemical factors related to pungency.