Step 2: Construction of a new dataset using the outputs (predictions/rescores) of the Pharmacophore models. Each model's output serves as base models and is combined to train Meta-models. In this context, we employed four types of data features for stacking optimization. Pharmacophore-predict stacking (PS) utilizes "prediction (0/1)" for optimization, while pharmacophore-score stacking (SS) relies on the "rescore" output. Best global stacking (GS) incorporates both "prediction" and "rescore" for optimization. Feature selection Stacking (FS) optimizes by selecting "prediction" and "rescore" through a feature selection algorithm.