Quantitative structure-activity relationship studies of RAR α, β, γ retinoid agonists
Structure‐Activity Relationships have been established for 140 synthetic retinoid agonists. Retinoids, natural and synthetic analogues of vitamin A, are activating ligands for Retinoic Acid Receptors (RAR α, β and γ), members of the nuclear receptor superfamily. A QSAR study provides information on...
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Veröffentlicht in: | Quantitative structure-activity relationships 1999-06, Vol.18 (2), p.107-123 |
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Sprache: | eng |
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Zusammenfassung: | Structure‐Activity Relationships have been established for 140 synthetic retinoid agonists. Retinoids, natural and synthetic analogues of vitamin A, are activating ligands for Retinoic Acid Receptors (RAR α, β and γ), members of the nuclear receptor superfamily. A QSAR study provides information on the type of intermolecular and intramolecular interactions the active molecules are exposed to during the course of their interaction with the receptor. Retinoid structures were modelled both by molecular and quantum mechanics and were submitted to a preliminary conformational analysis based on molecular dynamics. Linear and non‐linear multivariate analyses were performed, revealing the principal electronic and structural characteristics leading to good affinity for each RAR subtype. Distinct structural features were found for each subtype: this is in agreement with the fact that the selectivity of the RAR subtypes results from the change of amino acids in the ligand cavity. Indeed, these amino‐acids induce subtle changes in terms of steric properties and specific interactions, thus engendering specificity. The predictive ability of these relationships has been validated using a large set of compounds which were not used to derive the model. The goal this of work was to detect relationships between structures and affinity for a broad range of retinoids in order that this model could be used in a more general manner, for example to impose constraints in database searching, or for use in automatic structure generation software. |
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ISSN: | 0931-8771 1521-3838 |
DOI: | 10.1002/(SICI)1521-3838(199906)18:2<107::AID-QSAR107>3.0.CO;2-3 |