scholarly journals Quantitative Structure-Property Study on Pyrazines with Bell Pepper Flavor

2000 ◽  
Vol 68 (1) ◽  
pp. 41-56
Author(s):  
Ch. Th. Klein ◽  
H. Pircher ◽  
B. Wailzer ◽  
G. Buchbauer ◽  
P. Wolschann
Keyword(s):  
Biological Activity ◽  
Statistical Methods ◽  
Molecular Descriptors ◽  
Molecular Properties ◽  
Bell Pepper ◽  
Quantitative Structure ◽  
Structure Property ◽  
Qspr Study

A quantitative structure-property (QSPR) study on pyrazines with bell pepper aroma is performed by means of different statistical methods, which correlate appropriate molecular descriptors with the biological activity. The different methods lead to consistent results, indicating which of the molecular properties of the compounds under consideration are significant for bell pepper flavor. These results are compared with other models.

Studies on the EC50 of Natural Monoterpenes as Fungal Inhibitors with Quantitative Structure-Activity Relationships (QSARs)

2020 ◽  
Vol 10 (1) ◽  
pp. 44-60
Author(s):  
Mohamed E.I. Badawy ◽  
Entsar I. Rabea ◽  
Samir A.M. Abdelgaleil
Keyword(s):  
Biological Activity ◽  
Plant Pathogens ◽  
Molecular Descriptors ◽  
Microbial Pathogens ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Structure Activity ◽  
Wide Range

Background:Monoterpenes are the main constituents of the essential oils obtained from plants. These natural products offered wide spectra of biological activity and extensively tested against microbial pathogens and other agricultural pests.Methods:Antifungal activity of 10 monoterpenes, including two hydrocarbons (camphene and (S)- limonene) and eight oxygenated hydrocarbons ((R)-camphor, (R)-carvone, (S)-fenchone, geraniol, (R)-linalool, (+)-menthol, menthone, and thymol), was determined against fungi of Alternaria alternata, Botrytis cinerea, Botryodiplodia theobromae, Fusarium graminearum, Phoma exigua, Phytophthora infestans, and Sclerotinia sclerotiorum by the mycelia radial growth technique. Subsequently, Quantitative Structure-Activity Relationship (QSAR) analysis using different molecular descriptors with multiple regression analysis based on systematic search and LOOCV technique was performed. Moreover, pharmacophore modelling was carried out using LigandScout software to evaluate the common features essential for the activity and the hypothetical geometries adopted by these ligands in their most active forms.Results:The results showed that the antifungal activities were high, but depended on the chemical structure and the type of microorganism. Thymol showed the highest effect against all fungi tested with respective EC50 in the range of 10-86 mg/L. The QSAR study proved that the molecular descriptors HBA, MR, Pz, tPSA, and Vp were correlated positively with the biological activity in all of the best models with a correlation coefficient (r) ≥ 0.98 and cross-validated values (Q2) ≥ 0.77.Conclusion:The results of this work offer the opportunity to choose monoterpenes with preferential antimicrobial activity against a wide range of plant pathogens.

Biological Activity, Physical Properties, and Toxicity

2021 ◽  
Vol 6 (3) ◽  
pp. 25-34
Author(s):  
Ranita Pal ◽  
Pratim Kumar Chattaraj
Keyword(s):  
Molecular Structure ◽  
Biological Activity ◽  
Molecular Biology ◽  
Drug Design ◽  
Physical Properties ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Properties ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity

In the current pandemic-stricken world, quantitative structure-activity relationship (QSAR) analysis has become a necessity in the domain of molecular biology and drug design, realizing that it helps estimate properties and activities of a compound, without actually having to spend time and resources to synthesize it in the laboratory. Correlating the molecular structure of a compound with its activity depends on the choice of the descriptors, which becomes a difficult and confusing task when we have so many to choose from. In this mini-review, the authors delineate the importance of very simple and easy to compute descriptors in estimating various molecular properties/toxicity.

A quantitative structure–property relationship (QSPR) study of singlet oxygen generation by pteridines

2016 ◽  
Vol 15 (6) ◽  
pp. 801-811 ◽  
Author(s):  
Andrey A. Buglak ◽  
Taisiya A. Telegina ◽  
Mikhail S. Kritsky
Keyword(s):  
Dipole Moment ◽  
Singlet Oxygen ◽  
Quantum Yields ◽  
Quantitative Structure ◽  
Oxygen Production ◽  
Structure Property ◽  
Oxygen Generation ◽  
Singlet Oxygen Generation ◽  
Structure Property Relationship ◽  
Qspr Study

Singlet oxygen production quantum yields of pteridine photosensitizers were analyzed with the QSPR method. The ability of pterins and flavins to generate1O2in D2O correlated withEHOMOand electronegativity, as well as with the dipole moment and some other parameters.

Prediction of the Standard Enthalpy of Formation of Pure Compounds Using Molecular Structure

2009 ◽  
Vol 62 (4) ◽  
pp. 376 ◽  
Author(s):  
Farhad Gharagheizi
Keyword(s):  
Enthalpy Of Formation ◽  
Standard Enthalpy ◽  
Molecular Descriptors ◽  
Standard Enthalpy Of Formation ◽  
Quantitative Structure ◽  
Structure Property ◽  
Feed Forward Neural Network ◽  
Structure Property Relationship ◽  
Predictive Approach ◽  
Pure Compounds

A predictive approach has been presented to calculate the standard enthalpy of formation of pure compounds based on a quantitative structure–property relationship technique. A large number (1692) of pure compounds were used in this study. A genetic algorithm based on multivariate linear regression was used to subset variable selection. Using the selected molecular descriptors an optimized feed forward neural network was presented to predict the ΔHfo of pure compounds.

scholarly journals A New Accurate Neural Network Quantitative Structure- Property Relationship for Prediction of θ (Lower Critical Solution Temperature) of Polymer Solutions

e-Polymers ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Farhad Gharagheizi
Keyword(s):  
Neural Network ◽  
Molecular Descriptors ◽  
Polymer Solutions ◽  
Solution Temperature ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Relationship Model

Abstract In this study, a new neural network quantitative structure-property relationship model for prediction of θ (LCST ) of polymer solutions is presented. The parameters of this model are eight molecular descriptors which are calculated only from the chemical structure of polymer and solvent. These eight molecular descriptors were selected from 3328 molecular descriptors of polymer and solvent available in polymer solution by genetic algorithm-based multivariate linear regression (GA-MLR) technique. The obtained neural network model can predict the θ (LCST ) of 169 polymer solutions with mean relative error of 1.67% and squared correlation coefficient of 0.9736.

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