7 results on '"Dragon molecular descriptors"'
Search Results
2. Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
- Author
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Goodarzi, Mohammad, Ortiz, Erlinda V., Coelho, Leandro dos S., and Duchowicz, Pablo R.
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PESTICIDE pollution , *AIR analysis , *WATER analysis , *ALGORITHMS , *QSAR models , *BIOLOGICAL neural networks , *BAYESIAN analysis , *LINEAR statistical models , *NONLINEAR statistical models - Abstract
Abstract: This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative Structure–Property Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanol–water partition coefficient, and higher lipophilicities would lead to compounds having higher Henry’s law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the Levenberg–Marquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
3. New QSPR study for the prediction of aqueous solubility of drug-like compounds
- Author
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Duchowicz, Pablo R., Talevi, Alan, Bruno-Blanch, Luis E., and Castro, Eduardo A.
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SOLUTION (Chemistry) , *SOLUBILITY , *PHYSICAL & theoretical chemistry , *CARBON compounds - Abstract
Abstract: Solubility has become one of the key physicochemical screens at early stages of the drug development process. Solubility prediction through Quantitative Structure–Property Relationships (QSPR) modeling is a growing area of modern pharmaceutical research, being compatible with both High Throughput Screening technologies and limited compound availability characteristic of early stages of drug development. We resort to the QSPR theory for analyzing the aqueous solubility exhibited by 145 diverse drug-like organic compounds (0.781 being the average Tanimoto distances between all possible pairs of compounds in the training set). An accurate and generally applicable model is derived, consisting on a linear regression equation that involves three DRAGON molecular descriptors selected from more than a thousand available. Alternatively, we apply the linear QSPR to other 21 commonly employed validation compounds, leading to solubility estimations that compare fairly well with the performance achieved by previously reported Group Contribution Methods. [Copyright &y& Elsevier]
- Published
- 2008
- Full Text
- View/download PDF
4. QSAR modeling of the interaction of flavonoids with GABA(A) receptor
- Author
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Duchowicz, Pablo R., Vitale, Martín G., Castro, Eduardo A., Autino, Juan C., Romanelli, Gustavo P., and Bennardi, Daniel O.
- Subjects
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FLAVONOIDS , *LIGANDS (Biochemistry) , *BENZODIAZEPINES , *MOLECULAR structure , *QSAR models - Abstract
Abstract: Experimentally assigned values to binding affinity constants of flavonoid ligands towards the benzodiazepine site of the GABA(A) receptor complex were compiled from several publications, and enabled to perform a predictive analysis based on Quantitative Structure–Activity Relationships (QSAR). The best linear model established on 78 molecular structures incorporated four molecular descriptors, selected from more than a thousand of geometrical, topological, quantum-mechanical and electronic types of descriptors and calculated by Dragon software. An application of this QSAR equation was performed by estimating the binding affinities for some newly synthesized flavonoids displaying 2-,7-substitutions in the benzopyrane backbone which still do not have experimentally measured potencies. [Copyright &y& Elsevier]
- Published
- 2008
- Full Text
- View/download PDF
5. QSAR prediction of inhibition of aldose reductase for flavonoids
- Author
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Mercader, Andrew G., Duchowicz, Pablo R., Fernández, Francisco M., Castro, Eduardo A., Bennardi, Daniel O., Autino, Juan C., and Romanelli, Gustavo P.
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MOLECULAR structure , *GENETIC algorithms , *COMBINATORIAL optimization , *FLAVONOIDS - Abstract
Abstract: We performed a predictive analysis based on quantitative structure–activity relationships (QSAR) of an important property of flavonoids, which is the inhibition (IC50) of aldose reductase (AR). The importance of AR inhibition is that it prevents cataract formation in diabetic patients. The best linear model constructed from 55 molecular structures incorporated six molecular descriptors, selected from more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors. As a practical application, we used the obtained QSAR model to predict the AR inhibitory effect of newly synthesized flavonoids that present 2-, 7-substitutions in the benzopyrane backbone. [Copyright &y& Elsevier]
- Published
- 2008
- Full Text
- View/download PDF
6. QSAR prediction of inhibition of aldose reductase for flavonoids
- Author
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Daniel Oscar Bennardi, Francisco M. Fernández, Andrew G. Mercader, Pablo R. Duchowicz, Eduardo A. Castro, Gustavo P. Romanelli, and Juan Carlos Autino
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Aldose reductase inhibition ,Stereochemistry ,Flavone derivative ,Físico-Química, Ciencia de los Polímeros, Electroquímica ,Clinical Biochemistry ,Pharmaceutical Science ,Quantitative Structure-Activity Relationship ,Biochemistry ,Aldehyde Reductase ,Predictive Value of Tests ,Drug Discovery ,Cataract prevention ,Computer Simulation ,Molecular Biology ,Replacement method ,Flavonoids ,Molecular Structure ,Chemistry ,QSAR ,Flavone derivatives ,Enhanced replacement method ,Organic Chemistry ,Ciencias Químicas ,Dragon molecular descriptors ,Genetic algorithm ,Molecular Medicine ,Humanities ,CIENCIAS NATURALES Y EXACTAS - Abstract
We performed a predictive analysis based on quantitative structure–activity relationships (QSAR) of an important property of flavonoids, which is the inhibition (IC50) of aldose reductase (AR). The importance of AR inhibition is that it prevents cataract formation in diabetic patients. The best linear model constructed from 55 molecular structures incorporated six molecular descriptors, selected from more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors. As a practical application, we used the obtained QSAR model to predict the AR inhibitory effect of newly synthesized flavonoids that present 2-, 7-substitutions in the benzopyrane backbone50) of aldose reductase (AR). The importance of AR inhibition is that it prevents cataract formation in diabetic patients. The best linear model constructed from 55 molecular structures incorporated six molecular descriptors, selected from more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors. As a practical application, we used the obtained QSAR model to predict the AR inhibitory effect of newly synthesized flavonoids that present 2-, 7-substitutions in the benzopyrane backbone Fil: Mercader, Andrew Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina Fil: Fernández, Francisco Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina Fil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina Fil: Bennardi, Daniel Oscar. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Departamento de Ciencias Exactas. Cátedra de Química Orgánica; Argentina Fil: Autino, Juan Carlos. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Departamento de Ciencias Exactas. Cátedra de Química Orgánica; Argentina Fil: Romanelli, Gustavo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Departamento de Ciencias Exactas. Cátedra de Química Orgánica; Argentina
- Published
- 2008
7. QSAR modeling of the interaction of flavonoids with GABA(A) receptor
- Author
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Martín Gustavo Vitale, Pablo R. Duchowicz, Eduardo A. Castro, Gustavo Pablo Romanelli, Daniel Oscar Bennardi, and Juan Carlos Autino
- Subjects
Receptor complex ,Quantitative structure–activity relationship ,Molecular model ,Stereochemistry ,DRAGON MOLECULAR DESCRIPTORS ,Quantitative Structure-Activity Relationship ,Molecular descriptor ,Drug Discovery ,GABA(A) ,Binding site ,Binding affinities ,Pharmacology ,Flavonoids ,Binding Sites ,Molecular Structure ,Chemistry ,GABAA receptor ,QSAR ,Otras Ciencias Químicas ,Organic Chemistry ,Ciencias Químicas ,General Medicine ,FLUNITRAZEPAM ,Ligand (biochemistry) ,Receptors, GABA-A ,FLAVONE DERIVATIVE ,BENZODIAZEPINE RECEPTOR ,REPLACEMENT METHOD ,CIENCIAS NATURALES Y EXACTAS ,Protein Binding - Abstract
Experimentally assigned values to binding affinity constants of flavonoid ligands towards the benzodiazepine site of the GABA(A) receptor complex were compiled from several publications, and enabled to perform a predictive analysis based on Quantitative Structure-Activity Relationships (QSAR). The best linear model established on 78 molecular structures incorporated four molecular descriptors, selected from more than a thousand of geometrical, topological, quantum-mechanical and electronic types of descriptors and calculated by Dragon software. An application of this QSAR equation was performed by estimating the binding affinities for some newly synthesized flavonoids displaying 2-,7-substitutions in the benzopyrane backbone which still do not have experimentally measured potencies. Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina Fil: Vitale, Martin Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina Fil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina Fil: Autino, Juan Carlos. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina Fil: Romanelli, Gustavo Pablo. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; Argentina Fil: Bennardi, Daniel Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina
- Published
- 2007
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