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...
Guardado en:
Autor principal: | Ying Shi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/efc2fc9d3bf54a7fb4f364a855a3796a |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Prediction of xylanase optimal temperature by support vector regression
por: Zhang,Guangya, et al.
Publicado: (2012) -
Azole Compounds as Inhibitors of Candida albicans: QSAR Modelling
por: Davood Gheidari, et al.
Publicado: (2021) -
A novel approach for prediction of vitamin d status using support vector regression.
por: Shuyu Guo, et al.
Publicado: (2013) -
Phenolic Compounds in Honey and Their Relationship with Antioxidant Activity, Botanical Origin, and Color
por: Ana L. Becerril-Sánchez, et al.
Publicado: (2021) -
The effect of noise on the predictive limit of QSAR models
por: Scott S. Kolmar, et al.
Publicado: (2021)