Structural, QSAR, machine learning and molecular docking studies of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 receptor antagonists - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue New Journal of Chemistry Année : 2021

Structural, QSAR, machine learning and molecular docking studies of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 receptor antagonists

Résumé

We performed a structural study followed by theoretical analysis of the chemical descriptors and biological activity of a series of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 (CB1) receptor antagonists.

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Chimie
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Dates et versions

hal-04415142 , version 1 (24-01-2024)

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Riadh Hanachi, Ridha Ben Said, Hamza Allal, Seyfeddine Rahali, Mohammed Alkhalifah, et al.. Structural, QSAR, machine learning and molecular docking studies of 5-thiophen-2-yl pyrazole derivatives as potent and selective cannabinoid-1 receptor antagonists. New Journal of Chemistry, 2021, 45 (38), pp.17796-17807. ⟨10.1039/d1nj02261j⟩. ⟨hal-04415142⟩
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