Abstract
Abstract: Three-dimensional pharmacophore hypotheses were built from a set of 10 octopamine (OA) agonist 1-arylimidazole-2(3 H )-thiones (AIHTs) and 1-arylimidazolidine-2-thiones (AITs). Among the ten common-featured models generated by program Catalyst/HipHop, a hypothesis including a hydrophobic aromatic (HpAr), three hydrophobic aliphatic (HpAl) and a hydrogen-bond acceptor lipid (HBAl) features was considered to be important in evaluating the OA-agonist activity. Active OA agonist 2,6-Et 2 AIT mapped well onto all the HpAr, HpAl and HBAl features of the hypothesis. On the other hand, inactive compound 2,6-Et 2 AIHT was shown to be difficult to achieve the energetically favorable conformation which is found in the active molecules in order to fit the 3D common-feature pharmacophore models. The present studies on OA agonists demonstrate that an HpAr, three HpAls and an HBAl sites located on the molecule seem to be essential for OA-agonist activity. Keywords: Octopamine agonist, Common-feature hypothesis,
Highlights
Quantitative structure-activity relationship (QSAR) modeling is an area of research pioneered by Hansch and Fujita [1,2]
We described the use of Catalyst/Hypo to derive a 4- and 5-feature hypothesis from a set of 17 octopamine (OA) antagonists [4] and 43 agonists [5], respectively
OA-agonist activities of test compounds at several concentrations were examined using the adenylate-cyclase assay which was conducted on adult American cockroaches (P. americana L)
Summary
Quantitative structure-activity relationship (QSAR) modeling is an area of research pioneered by Hansch and Fujita [1,2]. The result of QSAR usually reflects as a predictive formula and attempts to model the activity of a series of compounds using measured or computed properties of the compounds. In the absence of such three-dimensional information, one may attempt to build a hypothetical model of the active site that can provide insight on the nature of the active site. Catalyst/Hypo is useful in building 3D pharmacophore models from the activity data and conformational structure. It can be used as an alternative for QSAR methods because of easy visualization and high prediction
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