Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine

Nantasenamat, Chanin; Isarankura‐Na‐Ayudhya, Chartchalerm; Naenna, Thanakorn; Prachayasittikul, Virapong

doi:10.1016/j.jmgm.2008.04.005

Cited by 52 publications

(29 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whether the sulfate group at specific positions of the Nap molecule may form resonance-stabilized intermediates or act as proton/electron/hydrogen donor to stabilize targeting free radical(s) will be an issue for further investigation. Further investigation on the O-H bond dissociation energy/enthalpies as well as stability of the target group at different position may provide useful clues (Pino et al, 2006;Nantasenamat et al, 2008). To gain insight into the stoichiometric relationship, we proceeded to determine the stoichiometric values of test samples as previously reported (Brand-Williams et al, 1995).…”

Section: Resultsmentioning

confidence: 99%

Radical scavenging effects of 1-naphthol, 2-naphthol, and their sulfate-conjugates

et al. 2018

View full text Add to dashboard Cite

-1-Naphthol (1-Nap) and 2-naphthol (2-Nap) are phenolic isomers that may be subjected to sulfate conjugation in vivo. Phase-II sulfate conjugation of phenolic compounds is generally thought to result in their inactivation. This study aimed to investigate the antioxidative effects of 1-NapS and 2-NapS, in comparison with their unsulfated counterparts, using established free radical scavenging assays. Based on the calculated EC 50 values, 1-NapS resulted in 5.60 to 7.35-times lower antioxidative activity than 1-Nap. In contrast, 2-NapS showed comparable activities as did the unsulfated 2-Nap. Collectively, the results obtained indicated that sulfate conjugation of the Nap isomers did not always result in the decrease of their antioxidant activity, and the antioxidant activity that remained appeared to depend on the position of sulfation.

show abstract

Section: Resultsmentioning

confidence: 99%

Radical scavenging effects of 1-naphthol, 2-naphthol, and their sulfate-conjugates

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The predictive performance of inhibition rates via all three predictive methods seems better than that of promotion rates. Besides the applications in the field of computational chemistry, SVM as a predictive guide tool has recently been employed in other researches such as antioxidant-related studies4243. In this study, we used SVR models to connect inhibition and promotion effects on the acrylamide formation with antioxidant properties and offer a statistical support for the probable correlation.…”

Section: Resultsmentioning

confidence: 99%

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

Huang

Wei

Wang

2016

Sci Rep

View full text Add to dashboard Cite

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1–10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633–0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926–0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation.

show abstract

“…Then, the second-layer weights are optimized, while the basis functions are kept fixed (supervised training). Finding apposite centers for the Gaussian function is the successful implementation key of these networks [12]. RBF network has a number of advantages over MLP.…”

Section: Radial Basis Function Neural Network (Rbf Nn)mentioning

confidence: 99%

“…Performance of the QSPR model heavily depends on the computational method adopted to build the model [3]. Many different methods, such as multiple linear regression (MLR) [2,4], partial least square analysis (PLS) [5,6], multilayer perceptrons (MLP) neural network [6,7], radial basis function neural network (RBF NN) [8] Adaptive neuro-fuzzy inference system (ANFIS) [9][10][11], and support vector machine (SVM) [3,12], have been used in QSPR models.…”

Section: Introductionmentioning

confidence: 99%

Diffusion coefficient prediction of acids in water at infinite dilution by QSPR method

Khajeh

Rasaei

2011

Struct Chem

View full text Add to dashboard Cite

In this article, quantitative structure-property relationship (QSPR) models were developed for estimation of diffusion coefficients (DCs) of acids at infinite dilution in water. These models were obtained based on molecular descriptors of a set of 65 compounds at 298.15 K. Five date splits are randomly extracted from this dataset. Using a genetic algorithm (GA) variable-selection approach, five molecular descriptors in each split were selected from a set of thousand of them. To analyze the nonlinear behavior of these molecular descriptors, Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function neural network (RBF NN) were used. These models were trained on 52 arbitrary sets of the data, and the remaining 13 sets were employed to evaluate the proposed models. Genetic function approximation (GFA) method could predict DCs of test dataset with high value of squared correlation coefficients of 0.984. QSPR model even gave superior predictions. ANFIS and RBF NN methods when implemented in QSPR model provided the squared correlation coefficients of 0.989 and 0.987, respectively. It is therefore suitable to be used in the proposed QSPR models, especially when applied with AN-FIS method, to obtain DC of acids at infinite dilution in water instead of other expensive and complicated experiments.

show abstract

Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine

Cited by 52 publications

References 49 publications

Radical scavenging effects of 1-naphthol, 2-naphthol, and their sulfate-conjugates

Radical scavenging effects of 1-naphthol, 2-naphthol, and their sulfate-conjugates

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

Diffusion coefficient prediction of acids in water at infinite dilution by QSPR method

Contact Info

Product

Resources

About