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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.


Scatter plot between the experimental TEAC of carotenoids reported by Müller et al. [4] and the predicted TEAC based on the quantum chemical descriptors calculated by PM6 (A) and PM7 (B) methods.
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pone.0140154.g003: Scatter plot between the experimental TEAC of carotenoids reported by Müller et al. [4] and the predicted TEAC based on the quantum chemical descriptors calculated by PM6 (A) and PM7 (B) methods.

Mentions: From the result of correlation analysis, I, Icat, and μcross were selected as dependent variables for developing QSAR models, for reason that these properties had a significant correlation with antioxidant activities of carotenoids. Most of previous QSAR studies developed the prediction models by linear regression [13,15,17,18,26]. However, traditional QSAR models by linear regression analysis ignore the interaction effects between dependent variables, thus this ignorance may have effects on the prediction efficiency of the model. The ANN applied techniques could be an alternative to conventional linear regression model. ANN based models are constructed and adjusted by empirical training process. It is useful to solve and analyse the problems which could not be solved easily with traditional linear regression analysis. Especially, ANN-based modelling is useful to analyse multivariate nonlinear relationship [39]. For this reason, a higher prediction efficiency could be achieved by applying empirical training-based ANN modelling technique on predicting bioactivities of molecules [40]. In this study, ANFIS, an ANN-based system, was used to achieve high prediction efficiency of QSAR models. At first, the ANFIS applied QSAR models were developed using a data set from Müller et al. [4]. The predicted TEAC values by the developed QSAR models were presented in Table 3. Because carotenoids were limited to a variety of naturally occurred and commercially available ones, data sets with a relatively small sample size were available to be analysed. Bootstrap validation is an appropriate method to validate models with a small sample size [41,42]. Thus, in this study, MAE and q-square values of developed models were estimated by the bootstrap method. Both of the QSAR models with quantum chemical properties calculated by the PM6 and PM7 semi-empirical methods had high prediction efficiencies (Table 3 and Fig 3).


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)

Scatter plot between the experimental TEAC of carotenoids reported by Müller et al. [4] and the predicted TEAC based on the quantum chemical descriptors calculated by PM6 (A) and PM7 (B) methods.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0140154.g003: Scatter plot between the experimental TEAC of carotenoids reported by Müller et al. [4] and the predicted TEAC based on the quantum chemical descriptors calculated by PM6 (A) and PM7 (B) methods.
Mentions: From the result of correlation analysis, I, Icat, and μcross were selected as dependent variables for developing QSAR models, for reason that these properties had a significant correlation with antioxidant activities of carotenoids. Most of previous QSAR studies developed the prediction models by linear regression [13,15,17,18,26]. However, traditional QSAR models by linear regression analysis ignore the interaction effects between dependent variables, thus this ignorance may have effects on the prediction efficiency of the model. The ANN applied techniques could be an alternative to conventional linear regression model. ANN based models are constructed and adjusted by empirical training process. It is useful to solve and analyse the problems which could not be solved easily with traditional linear regression analysis. Especially, ANN-based modelling is useful to analyse multivariate nonlinear relationship [39]. For this reason, a higher prediction efficiency could be achieved by applying empirical training-based ANN modelling technique on predicting bioactivities of molecules [40]. In this study, ANFIS, an ANN-based system, was used to achieve high prediction efficiency of QSAR models. At first, the ANFIS applied QSAR models were developed using a data set from Müller et al. [4]. The predicted TEAC values by the developed QSAR models were presented in Table 3. Because carotenoids were limited to a variety of naturally occurred and commercially available ones, data sets with a relatively small sample size were available to be analysed. Bootstrap validation is an appropriate method to validate models with a small sample size [41,42]. Thus, in this study, MAE and q-square values of developed models were estimated by the bootstrap method. Both of the QSAR models with quantum chemical properties calculated by the PM6 and PM7 semi-empirical methods had high prediction efficiencies (Table 3 and Fig 3).

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.