Quantitative structure-retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae)

Driouche, Youssouf; Messadi, Djelloul

doi:10.2298/jsc180817010d

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Statistical models that quantify the relation between the structure of molecules and their chromatographic RIs were represented by the quantitative structure retention relationship (QSRR) model [ 58 , 64 ]. Numerous publications related to the QSRR analysis in plants from Lamiaceae family could be found in the literature: Thymus vulgaris [ 65 ], T. serpyllum [ 66 ], Satureja kitaibelii [ 55 ], Salvia officinalis [ 67 ], as well as Stachys sp. [ 68 ].…”

Section: Discussionmentioning

confidence: 99%

Chemical Characterization of Marrubium vulgare Volatiles from Serbia

Aćimović

Ivanović

Simić

et al. 2021

Plants

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Marrubium vulgare is a cosmopolitan medicinal plant from the Lamiaceae family, which produces structurally highly diverse groups of secondary metabolites. A total of 160 compounds were determined in the volatiles from Serbia during two investigated years (2019 and 2020). The main components were E-caryophyllene, followed by germacrene D, α-humulene and α-copaene. All these compounds are from sesquiterpene hydrocarbons class which was dominant in both investigated years. This variation in volatiles composition could be a consequence of weather conditions, as in the case of other aromatic plants. According to the unrooted cluster tree with 37 samples of Marrubium sp. volatiles from literature and average values from this study, it could be said that there are several chemotypes: E-caryophyllene, β-bisabolene, α-pinene, β-farnesene, E-caryophyllene + caryophyllene oxide chemotype, and diverse (unclassified) chemotypes. However, occurring polymorphism could be consequence of adaptation to grow in different environment, especially ecological conditions such as humidity, temperature and altitude, as well as hybridization strongly affected the chemotypes. In addition, this paper aimed to obtain validated models for prediction of retention indices (RIs) of compounds isolated from M. vulgare volatiles. A total of 160 experimentally obtained RIs of volatile compounds was used to build the prediction models. The coefficients of determination were 0.956 and 0.964, demonstrating that these models could be used for predicting RIs, due to low prediction error and high r2.

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Section: Discussionmentioning

confidence: 99%

Chemical Characterization of Marrubium vulgare Volatiles from Serbia

Aćimović

Ivanović

Simić

et al. 2021

Plants

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“…VIF values corresponding to unity depict no inter-correlation among each variable, or if the VIF scores range between 1 and 5, the model is acceptable and stable. But, if the VIF scores are larger than 10, it means that the model in question is unstable and unacceptable [18]. Tables 5 and 6 show the correlation matrix and VIF scores of the descriptors used in forming the selected model.…”

Section: Discussionmentioning

confidence: 99%

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

Abdullahi

Shallangwa

Uzairu

2020

Beni-Suef Univ J Basic Appl Sci

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Background: This research provides a comprehensive analysis of QSAR modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC 50) against H5N1 influenza A virus by using some numerical information derived from structural and chemical features (descriptors) of the compounds to generate a statistically significant model. Subsequently, the molecular docking simulations were done so as to determine the binding modes of some potent ligands in the dataset with the M2 proton channel protein of the H5N1 influenza A virus as the target. Results: In building the QSAR model, the genetic algorithm task was employed in the variable selection of the descriptors which are used to form the multi-linear regression equation. The model with descriptors, RDF100m, nO, and RDF45p, showed satisfactory internal and external validation parameters (R 2 train = 0.72963, R 2 adjusted = 0.67169, Q 2 cv = 0.598, R 2 pred ¼ 0.67295, R 2 test = 0.6860) which passed the model criteria of acceptability. Docking simulation results of the more potent compounds (ligands 2, 3, and 8) revealed the formation of hydrophobic and hydrogen bonds with the binding pockets of M2 protein of influenza A virus. Conclusion: The results in this study can help to advance the research in designing (in silico design) and synthesis of more potent aryl sulfonamides derivatives against H5N1 influenza virus.

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“…Essential oils are a complex mixture of volatile, low molecular weight organic compounds. Correlations between the GC retention indices of these compounds and their molecular structures can provide very important information about the effects and the possible mechanisms of absorption and elution [36]. However, molecular size is an important descriptor as most physicochemical properties and many biological properties are strongly size-related.…”

Section: Qsrr Modelsmentioning

confidence: 99%

A Validation Model for Prediction of Kovats Retention Indices of Compounds Isolated from Origanum spp. and Thymus spp. Essential Oils

et al. 2021

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Abstract. This work aimed to obtain a validated model for the prediction of retention times of compounds isolated from Origanum heracleoticum, Origanum vulgare, Thymus vulgaris, and Thymus serpyllum essential oils. In total 68 experimentally obtained retention times of compounds, which were separated and detected by GC-MS were further used to build the prediction models. The quantitative structure–retention relationship was employed to foresee the Kovats retention indices of compounds acquired by GC-MS analysis, using eight molecular descriptors selected by a genetic algorithm. The chosen descriptors were used as inputs for the four artificial neural networks, to construct a Kovats retention indices predictive quantitative structure–retention relationship model. The coefficients of determination in the training cycle were 0.830; 0.852; 0.922 and 0.815 (for compounds found in O. heracleoticum, O. vulgare, T. vulgaris and T. serpyllum essential oils, respectively), demonstrating that these models could be used for prediction of Kovats retention indices, due to low prediction error and high r2. Resumen. El objetivo de este trabajo es la obtención de modelos validados para la predicción del tiempo de retención de los compuestos aislados de aceites esenciales de Origanum heracleoticum, Origanum vulgare, Thymus vulgaris y Thymus serpyllum. Se han obtenido un total de 68 tiempos de retención de compuestos, separándose y detectándose por cromatografía de gases con detección por espectrometría de masas (GC-MS) con posterior desarrollo de modelos de predicción. La relación cuantitativa estructura-retención ha sido utilizada para predecir el índice de retención Kovats de los compuestos obtenidos por análisis de GC-MS, utilizando ocho descriptores moleculares seleccionados mediante algoritmo genético. Los descriptores seleccionados han sido utilizados como entrada para las cuatro redes neuronales artificiales y así elaborar los índices predictivos del modelo de relación cuantitativa estructura-retención. Los coeficientes de determinación en el ciclo de entrenamiento fueron de 0.830; 0.852; 0.922 y 0.815 (para los compuestos identificados en los aceites esenciales del O. heracleoticum, O. vulgare, T. vulgaris y T. serpyllum respectivamente) demostrando así que estos modelos son útiles en la predicción de los índices de retención de Kovats con un error de bajo predicción y alta r2.

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Quantitative structure-retention relationship model for predicting retention indices of constituents of essential oils of Thymus vulgaris (Lamiaceae)

Cited by 7 publications

References 29 publications

Chemical Characterization of Marrubium vulgare Volatiles from Serbia

Chemical Characterization of Marrubium vulgare Volatiles from Serbia

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

A Validation Model for Prediction of Kovats Retention Indices of Compounds Isolated from Origanum spp. and Thymus spp. Essential Oils

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