Computational QSAR study of novel 2-aminothiazol-4(5H)-one derivatives as 11β-HSD1 inhibitors

This research aims to develop a predictive model to support the design of new 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) inhibitors based on the pseudothiohydantoin scaffold, offering potential for novel treatments of metabolic disorders. The Quantitative Structure-Activity Relationship (QSAR) analysis was performed on 56 2-aminothiazol-4(5h)-one derivatives, for which the 11β-HSD1 inhibitory activity was previously reported. Gaussian software was employed for geometry optimization, while Dragon software was used to calculate the molecular descriptors. The study used an Artificial Neural Network (ANN) algorithm for regression analysis between the top ten preselected descriptors and the activity of the studied analogs. A predictive model was developed using a network architecture 10-11-1 with a Broyden-Fletcher-Goldfarb-Shanno learning algorithm. The model's reliability was supported through cross-validation and y-randomization strategies. The model exhibited high accuracy with a determination coefficient (R²) of 0.9482, and its validity was confirmed through internal validation with a cross-validated R² (Q²) of 0.9944. Three classes of 3D descriptors (GETAWAY, 3D-MoRSE, and RDF descriptors) and four groups of topological indices (GALVEZ, 2D autocorrelations, 2D matrix-based descriptors, and Burden eigenvalues) were used to generate the QSAR model. The developed model was applied to predict the 11β-HSD1 inhibitory activity of four designed series of 2-aminothiazol-4(5h)-one derivatives, suggesting that compounds with cyclohexyl and 2-(tetrahydro-2H-pyran-2-yl)methyl residues substituted at the amino group, and various substituents at C-5 of the thiazole ring, could be potential candidates for upcoming chemical synthesis and biological assessment.