Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds
Abstract The Support vector regression (SVR) was used to investigate quantitative structure–activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson corr...
Main Author: | Ying Shi |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2021-04-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-021-88341-1 |
Similar Items
-
QSAR model for pka prediction of phenols
by: Hakim Hamada
Published: (2023-02-01) -
QSAR study of phenolic compounds and their anti-DPPH radical activity by discriminant analysis
by: Ang Lu, et al.
Published: (2022-05-01) -
QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression
by: Rachid Darnag, et al.
Published: (2017-02-01) -
Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy
by: Mario-Livio Jeličić, et al.
Published: (2022-06-01) -
Antioxidant activity of phenolic compounds
by: R. Maestro Durán, et al.
Published: (1993-04-01)