In this study, a 5-point pharmacophore model was developed and the model was used to generate a predictive atom-based 3D quantitative structure activity relationship (3D-QSAR) analysis for the studied dataset of 50 compounds. The obtained 3D-QSAR model shows correlation coefficient (R(2)) of 0.87 for training set compounds and excellent predictive power (Q(2)) of 0.81 for cross-validated test set compounds. External validation indicated that our 3D-QSAR model has high predictive power with [Formula: see text] and [Formula: see text] values of 0.99 and 0.65, respectively. The most active and least active compounds were further optimized using density functional theory at B3LYP/3-21*G level. Further, pharmacophoric model was employed for pharmacophore-based screening to identify potential inhibitors against Wnt/â-catenin pathway. Hence, these molecules could act as selective inhibitors of Wnt/â-catenin pathway which can be experimentally validated. The backbone of these inhibitors could serve as templates for designing drug-like molecules specifically targeting Wnt/â-catenin pathway.
Drug Design Humans