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Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

Jhin C, Hwang KT - PLoS ONE (2015)

Bottom Line: Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study.The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively.The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

View Article: PubMed Central - PubMed

Affiliation: Department of Food and Nutrition, Research Institute of Human Ecology, Seoul National University, Seoul, Korea.

ABSTRACT
One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

No MeSH data available.


Developed adaptive neuro-fuzzy inference system (ANFIS) structure.
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pone.0140154.g002: Developed adaptive neuro-fuzzy inference system (ANFIS) structure.

Mentions: Two triangular-shaped membership functions were applied for each independent variable, 8 if-then rules and 8 linear type output functions were applied for ANFIS. To train and optimise ANFIS models, back-propagation method was used. The structure of ANFIS is illustrated on Fig 2. To validate constructed QSAR models, 1000 times of bootstrap resampling validation procedure were applied. To measure prediction efficiency, mean absolute error (MAE) was calculated as follows:MAE=1N∑i=1n/yi′−yi/(7)where is predicted value resulted from QSAR model and yi is experimental value from the literature.


Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

Jhin C, Hwang KT - PLoS ONE (2015)

Developed adaptive neuro-fuzzy inference system (ANFIS) structure.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608816&req=5

pone.0140154.g002: Developed adaptive neuro-fuzzy inference system (ANFIS) structure.
Mentions: Two triangular-shaped membership functions were applied for each independent variable, 8 if-then rules and 8 linear type output functions were applied for ANFIS. To train and optimise ANFIS models, back-propagation method was used. The structure of ANFIS is illustrated on Fig 2. To validate constructed QSAR models, 1000 times of bootstrap resampling validation procedure were applied. To measure prediction efficiency, mean absolute error (MAE) was calculated as follows:MAE=1N∑i=1n/yi′−yi/(7)where is predicted value resulted from QSAR model and yi is experimental value from the literature.

Bottom Line: Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study.The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively.The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

View Article: PubMed Central - PubMed

Affiliation: Department of Food and Nutrition, Research Institute of Human Ecology, Seoul National University, Seoul, Korea.

ABSTRACT
One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

No MeSH data available.