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Julio Caballero

Showing results (1-10 of 140) with videos related to

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Journal of Molecular Graphics & Modelling|September 25, 2010
3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compoundsJulio Caballero
Molecules (Basel, Switzerland)|January 17, 2020
Considerations for Docking of Selective Angiotensin-Converting Enzyme InhibitorsJulio Caballero
Molecules (Basel, Switzerland)|April 3, 2021
Computational Modeling to Explain Why 5,5-Diarylpentadienamides are TRPV1 AntagonistsJulio Caballero
Expert Opinion on Drug Discovery|December 23, 2020
The latest automated docking technologies for novel drug discoveryJulio Caballero
Journal of Enzyme Inhibition and Medicinal Chemistry|August 17, 2022
A new era for the design of TRPV1 antagonists and agonists with the use of structural information and molecular docking of capsaicin-like compoundsJulio Caballero
Bioorganic & Medicinal Chemistry|June 26, 2007
QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-alpha-phenylsulfonylacetamide derivativesMichael Fernández, Julio Caballero
Molecules (Basel, Switzerland)|May 2, 2018
Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?David Ramírez, Julio Caballero
International Journal of Molecular Sciences|April 23, 2016
Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target?David Ramírez, Julio Caballero
Journal of Molecular Graphics & Modelling|April 1, 2006
Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptorMichael Fernández, Julio Caballero
Current Topics in Medicinal Chemistry|December 17, 2008
Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1)Julio Caballero, Michael Fernández
Pageof 14

Showing results (1-10 of 140) with videos related to

Sort By:
Pageof 14
Journal of Molecular Graphics & Modelling|September 25, 2010
3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compoundsJulio Caballero
Molecules (Basel, Switzerland)|January 17, 2020
Considerations for Docking of Selective Angiotensin-Converting Enzyme InhibitorsJulio Caballero
Molecules (Basel, Switzerland)|April 3, 2021
Computational Modeling to Explain Why 5,5-Diarylpentadienamides are TRPV1 AntagonistsJulio Caballero
Expert Opinion on Drug Discovery|December 23, 2020
The latest automated docking technologies for novel drug discoveryJulio Caballero
Journal of Enzyme Inhibition and Medicinal Chemistry|August 17, 2022
A new era for the design of TRPV1 antagonists and agonists with the use of structural information and molecular docking of capsaicin-like compoundsJulio Caballero
Bioorganic & Medicinal Chemistry|June 26, 2007
QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-alpha-phenylsulfonylacetamide derivativesMichael Fernández, Julio Caballero
Molecules (Basel, Switzerland)|May 2, 2018
Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?David Ramírez, Julio Caballero
International Journal of Molecular Sciences|April 23, 2016
Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target?David Ramírez, Julio Caballero
Journal of Molecular Graphics & Modelling|April 1, 2006
Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptorMichael Fernández, Julio Caballero
Current Topics in Medicinal Chemistry|December 17, 2008
Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1)Julio Caballero, Michael Fernández
Pageof 14