Innovative Integration of Butina Clustering with Ensemble Learning Techniques for the Refined Pharmacophore Modeling of Apelin Receptor Agonists: A High-Impact Computational Approach

3D pharmacophore models describe the ligand’s chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in Drug Design. Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The ensemble learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by receiver operating characteristic, enrichment factor, Güner-Henry score, and F-measure. Although one of the high-scoring models achieved statistically superior results in each dataset, the ensemble learning method including Voting and Stacking method balanced the shortcomings of each model and passed with close performance measures.