The reason was the severe imbalance between the active and the decoy set (ratio 1:50), which led to the extremely low precision of the model, on which the GH and F1-score depended largely. The pharmacophoric feature types of RHHa_52 (Fig. ) was represented by geometric entities and included 2 hydrophobic and 2 hydrogen bond acceptors. The study set RHHa_52 as the base model to compare the performance of the Stacking optimization. However, the disadvantage of single Pharmacophore model is that it uses a single conformation of each substance. In fact, at the binding site, substances could exist in numerous different conformations. Therefore, combining Pharmacophore models will be able to solve the problem of diverse conformations in virtual screening.