F1-score of 10 models were better than the base one (RHHa_52 The predictive output of the Pharmacophore model is not only a binary variable (0/1) but also a score (rescore). These scores were used to optimize using Pharmacophore-score Stacking (SS) method. When optimizing train set D (threshold AUC ≥ 0.6), the SS method improved the AUC score by an average of 0.992±0.008 to important elements to optimize. The results showed that the AUC, GH, and F1-score of the FS method were not statistically significant by the Wilcoxon test. All the internalvalidated comparisons between base model and optimized models were shown in Fig. (A, B, C). To illustrate the model's generalizability, external validation of four optimal methods was depicted in Fig. . The outcomes indicated an increase in AUC, GH, and F1-score compared to the base model RHHa_52. Besides, the enrichment factor EF1% also increased from 18.333% (RHHa_52) to 50%. These high values of AUC, EF, GH, and F1-score referred to the good ability of the pharmacophore models to identify active compounds