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A test series of 32 phenylpiperazines III with affinity for 5-HT1A and alpha1 receptors was subjected to QSAR analysis using artificial neural networks (ANNs), in order to get insight into the structural requirements that are responsible for 5-HT1A/alpha1 selectivity. Good models and predictive power were obtained for 5-HT1A and alpha1 receptors. A comparison of these models gives information for the design of the new ligand EF-7412 (5-HT1A:Ki(nM)= 27; alpha1: Ki(nM) > 1000). This derivative displayed affinity for dopamine D2 receptor (Ki = 22 nM) and is selective for all other receptor examined (5-HT2A, 5-HT3, 5-HT4 and Bz). EF-7412 acts an antagonist in vivo in pre- and postsynaptic 5-HT1A receptor sites and as an antagonist in dopamine D2 receptor.  相似文献   

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QSAR models represent the relationship of biological activity with either physicochemical parameters or structural indices. QSAR study was performed on some arylpiperazines as 5-HT(1A)/alpha(1)-adrenergic receptor antagonists using E-state indices to identify the pharmacophoric requirements. It was found that some of the atoms played important roles to both activities and some played important role in selectivity of compound to the 5-HT(1A) antagonistic activity. The presence of COONHPr group at the ortho-position of the phenyl ring might be disadvantageous and Br at meta-position might be conducive to the activity. COOPr at the ortho-position might be disfavored the adrenergic alpha(1)-antagonistic activity, thus increase the selectivity.  相似文献   

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