Before a pharmacophore model is used in virtual screening, it's crucial to validate it. The model's quality can be assessed using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The ROC curve shows the model's ability to distinguish between active and inactive compounds by plotting the true positive rate against the false positive rate. If the curve is sharp and flat, it means the model ranks active compounds higher than inactive ones. The AUC is a measure of the pharmacophore's performance and is useful when evaluating multiple models. The AUC can range from 0 to 1, with 1 representing an ideal case where all active compounds are correctly identified first, 0 indicating all inactive compounds are incorrectly classified as active, and 0.5 representing a random state . Alongside validation metrics, the performance of the model is also evaluated using the statistical parameters of Güner-Henry (GH) , enrichment factor (EF) , and F-measure . The GH score, which ranges from 0 (representing a null model) to 1 (representing an ideal model), is deemed acceptable if it's higher than 0.7. Conceptually, EF score measures the number of active compounds found in the "early recognition" portion, calculated by determining the ratio of the number of true positive compounds (active compounds)