Quantitative structure–activity relationships for estimating the aryl hydrocarbon receptor binding affinities of resveratrol derivatives and the antioxidant activities of hydroxystilbenes

2009 ◽  
Vol 19 (8) ◽  
pp. 864-901 ◽  
Author(s):  
Sierra Rayne ◽  
Charles D. Goss ◽  
Kaya Forest ◽  
Ken J. Friesen
Keyword(s):  
Aryl Hydrocarbon Receptor ◽  
Receptor Binding ◽  
Antioxidant Activities ◽  
Binding Affinities ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Aryl Hydrocarbon ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships ◽  
Resveratrol Derivatives

scholarly journals Non-Linear Quantitative Structure–Activity Relationships Modelling, Mechanistic Study and In-Silico Design of Flavonoids as Potent Antioxidants

2019 ◽  
Vol 20 (9) ◽  
pp. 2328 ◽  
Author(s):  
Petar Žuvela ◽  
Jonathan David ◽  
Xin Yang ◽  
Dejian Huang ◽  
Ming Wah Wong
Keyword(s):  
Electron Transfer ◽  
Antioxidant Activities ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Non Linear ◽  
Quantitative Structure Activity Relationships ◽  
Mean Square Errors ◽  
Proton Loss

In this work, we developed quantitative structure–activity relationships (QSAR) models for prediction of oxygen radical absorbance capacity (ORAC) of flavonoids. Both linear (partial least squares—PLS) and non-linear models (artificial neural networks—ANNs) were built using parameters of two well-established antioxidant activity mechanisms, namely, the hydrogen atom transfer (HAT) mechanism defined with the minimum bond dissociation enthalpy, and the sequential proton-loss electron transfer (SPLET) mechanism defined with proton affinity and electron transfer enthalpy. Due to pronounced solvent effects within the ORAC assay, the hydration energy was also considered. The four-parameter PLS-QSAR model yielded relatively high root mean square errors (RMSECV = 0.783, RMSEE = 0.668, RMSEP = 0.900). Conversely, the ANN-QSAR model yielded considerably lower errors (RMSEE = 0.180 ± 0.059, RMSEP1 = 0.164 ± 0.128, and RMSEP2 = 0.151 ± 0.114) due to the inherent non-linear relationships between molecular structures of flavonoids and ORAC values. Five-fold cross-validation was found to be unsuitable for the internal validation of the ANN-QSAR model with a high RMSECV of 0.999 ± 0.253; which is due to limited sample size where resampling with replacement is a considerably better alternative. Chemical domains of applicability were defined for both models confirming their reliability and robustness. Based on the PLS coefficients and partial derivatives, both models were interpreted in terms of the HAT and SPLET mechanisms. Theoretical computations based on density functional theory at ωb97XD/6-311++G(d,p) level of theory were also carried out to further shed light on the plausible mechanism of anti-peroxy radical activity. Calculated energetics for simplified models (genistein and quercetin) with peroxyl radical derived from 2,2′-azobis (2-amidino-propane) dihydrochloride suggested that both SPLET and single electron transfer followed by proton loss (SETPL) mechanisms are competitive and more favorable than HAT in aqueous medium. The finding is in good accord with the ANN-based QSAR modelling results. Finally, the strongly predictive ANN-QSAR model was used to predict antioxidant activities for a series of 115 flavonoids designed combinatorially with flavone as a template. Structural trends were analyzed, and general guidelines for synthesis of new flavonoid derivatives with potentially potent antioxidant activities were given.

QSAR Modeling and Prediction of Triptan Binding Affinities

2021 ◽  
Vol 6 (2) ◽  
pp. 19-28
Author(s):  
Lucas Alland ◽  
Solomon H. Jacobson
Keyword(s):  
Neural Network ◽  
Binding Affinities ◽  
Potential Drug ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Structure Activity Relationships ◽  
Drug Candidates ◽  
Modeling And Prediction ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

The purpose of this study was to use quantitative structure-activity relationships (QSARs) to identify new triptan molecules that selectively bind to h 5-HT1B and h 5-HT1D to a greater extent than to the similar h 5-HT1A receptor in order to identify novel compounds that could lead to an alternative and potentially superior migraine relief drug. Due to its possibility in migraine abortive properties, binding affinities to h 5-HT1F were also modeled. Binding affinities for 12 different triptan drugs and structurally similar substances were compiled from the literature, and descriptors were generated for those and other potential drug candidates using a variety of programs. The most significant descriptors were identified using a stepwise model, and the final QSARs were built for each activity with those descriptors, and a neural network. QSARs for all four activities were validated using a holdback method and were all found to be highly accurate. With these QSARs, activities of novel compounds similar to triptan drugs were predicted and three potential drug candidates were suggested.

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