The ligand-based 3D-pharmacophore approach, extensively utilized in virtual drug screening, constructs predictive models from active datasets, drawing upon the study of 3D configurations and interactions among functional molecules . Nevertheless, the efficacy of pharmacophore models is intrinsically tied to the diversity and precision of the input training dataset. Consequently, data clustering is implemented via the Butina algorithm to amass the centroids prior to the construction of pharmacophore models in Pharmacophore Elucidator -MOE 2015.10 6 . The centroids derived from each active cluster are collated to formulate a structural training dataset, while the remaining active compounds are utilized in decoy generation by the DeepCoy model . DeepCoy fabricates high-caliber decoys that pose a challenge to differentiate from active substances, thereby mitigating the risk of artificial enrichment, analog bias, and false negative bias. Artificial enrichment pertains to the performance influenced by the disparities in chemical space between the active and decoy molecules. Analog bias emerges from the restricted diversity of the active molecules, while false negative bias refers to the potential presence of active compounds within the decoy set, which could result in an underestimation of the screening performance.