1. QSAR studies and pharmacophore identification for arylsubstituted cycloalkenecarboxylic acid methyl esters with affinity for the human dopamine transporter
- Author
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Søren V. Boye, Mikael Bols, Helena Staunstrup Christensen, Ove Wiborg, Steffen Sinning, Jacob Thinggaard, and Birgit Schiøtt
- Subjects
Models, Molecular ,Quantitative structure–activity relationship ,Molecular model ,Stereochemistry ,Clinical Biochemistry ,Carboxylic Acids ,Pharmaceutical Science ,Quantitative Structure-Activity Relationship ,Plasma protein binding ,Biochemistry ,Dopamine ,Drug Discovery ,medicine ,Humans ,Computer Simulation ,Molecular Biology ,HOMO/LUMO ,Dopamine transporter ,Dopamine Plasma Membrane Transport Proteins ,biology ,Molecular Structure ,Chemistry ,Inhibitors ,QSAR ,Organic Chemistry ,Cycloparaffins ,Monoamine transporters ,biology.protein ,Molecular Medicine ,Monoamine transport ,Pharmacophore ,medicine.drug ,Protein Binding - Abstract
Data from a series of 29 monoamine transport inhibitors were used to generate 2D and 3D QSAR models for their binding affinity to the human dopamine transporter (hDAT). Among the inhibitors were many non-nitrogen containing compounds. The 2D QSAR analysis resulted in the equation −log Ki = 4.00 − 3.93ELUMO − 0.67EHOMO − 3.24σp, which predicted the importance of electron withdrawing groups in the aromatic moiety. However, the model failed to predict the observed poor binding of nitro-substituted compounds. In contrast, a derived 3D QSAR model was capable of predicting these more correctly. Data from a series of 29 monoamine transport inhibitors were used to generate 2D and 3D QSAR models for their binding affinity to the human dopamine transporter (hDAT). Among the inhibitors were many non-nitrogen containing compounds. The 2D QSAR analysis resulted in the equation -log Ki = 4.00 - 3.93ELUMO - 0.67EHOMO - 3.24σp, which predicted the importance of electron withdrawing groups in the aromatic moiety. However, the model failed to predict the observed poor binding of nitro-substituted compounds. In contrast, a derived 3D QSAR model was capable of predicting these more correctly.
- Published
- 2007
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