Prediction of chromatographic parameters for some anilines by molecular connectivity

Pérez-Giménez, Facundo; Antón-Fos, G.M.; García-March, F. J.; Salabert-Salvador, Ma Teresa; Cercós-del-Pozo, R. A.; Jaén-Oltra, J.

doi:10.1007/bf02267949

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…A large number of quantitative structure−activity relationship (QSAR) studies has been reported in recent literature that use theoretical molecular descriptors in predicting the physicochemical, pharmacological, and toxicological properties of molecules. − The important common feature of all those descriptors is the independence of their numerical values on renumbering atoms in a chemical structure. To perform quantitative “structure−activity” and “structure−property” (QSAR/QSPR) studies correctly, chemists have had to design a variety of molecular graph invariants. − …”

Section: Introductionmentioning

confidence: 99%

QSAR Analysis of Hypoglycemic Agents Using the Topological Indices

Murcia-Soler

Pérez-Giménez

Nalda-Molina

et al. 2001

J. Chem. Inf. Comput. Sci.

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The molecular topology model and discriminant analysis have been applied to the prediction of some pharmacological properties of hypoglycemic drugs using multiple regression equations with their statistical parameters. Regression analysis showed that the molecular topology model predicts these properties. The corresponding stability (cross-validation) studies performed on the selected prediction models confirmed the goodness of the fits. The method used for hypoglycemic activity selection was a linear discriminant analysis (LDA). We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and selection of new hypoglycemic agents, and we tested on rats the predictive ability of the model.

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Section: Introductionmentioning

confidence: 99%

QSAR Analysis of Hypoglycemic Agents Using the Topological Indices

Murcia-Soler

Pérez-Giménez

Nalda-Molina

et al. 2001

J. Chem. Inf. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…A large number of quantitative structure−activity relationship (QSAR) studies have been reported in recent literature that use theoretical molecular descriptors in predicting the physicochemical, pharmacological, and toxicological properties of molecules. − The important common feature of all those descriptors is the independence of their numerical values on renumbering atoms in a chemical structure. To perform quantitative “structure−activity” and “structure−property” (QSPR) studies correctly, chemists have had to design a variety of molecular graph invariants. − Therefore, it becomes necessary to develop QSARs based on nonempirical parameters which can predict the biological properties for a homogeneous collection of chemicals so that such models are generally applicable.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Applied to Prediction of Fluorquinolone Antibacterial Activity by Topological Methods

Jaén-Oltra

et al. 2000

J. Med. Chem.

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A new topological method that makes it possible to predict the properties of molecules on the basis of their chemical structures is applied in the present study to quinolone antimicrobial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. This makes it possible to determine the minimal inhibitory concentration (MIC) of quinolones. Analysis of the results shows that the experimental and calculated values are highly similar. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried out.

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“…Molecular connectivity is a topological method able to describe the structure of a molecule by means of numbers called indices ( m χ t ), calculated from the hydrogen-suppressed graphs of the molecule being studied. These indices subsequently regress in relation to the experimental values of the physical, chemical, and/or biological , properties to obtain the so-called connectivity functions…”

mentioning

confidence: 99%

Discrimination and Molecular Design of New Theoretical Hypolipaemic Agents Using the Molecular Connectivity Functions

Cercós-del-Pozo

Pérez-Giménez

Salabert-Salvador

et al. 1999

J. Chem. Inf. Comput. Sci.

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The molecular topology model and discriminant analysis have been applied to the prediction and QSAR interpretation of some pharmacological properties of hypolipaemic drugs using multivariable regression equations with their statistical parameters. Regression analysis showed that the molecular topology model predicts these properties. The corresponding stability (cross-validation) studies done on the selected prediction models confirmed the goodness of the fits. The method used for hypolipaemic activity selection was a linear discriminant analysis (LDA). We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and design of new hypolipaemic agents.

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Prediction of chromatographic parameters for some anilines by molecular connectivity

Cited by 3 publications

References 6 publications

QSAR Analysis of Hypoglycemic Agents Using the Topological Indices

QSAR Analysis of Hypoglycemic Agents Using the Topological Indices

Artificial Neural Network Applied to Prediction of Fluorquinolone Antibacterial Activity by Topological Methods

Discrimination and Molecular Design of New Theoretical Hypolipaemic Agents Using the Molecular Connectivity Functions

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