QSAR and Molecular Docking Studies for Anticancer Drug Targeting BCL-2

The anti-apoptotic protein B-cell lymphoma 2 (BCL-2) has emerged as a critical therapeutic target in cancer due to its role in evading programmed cell death. Inhibiting BCL-2 restores apoptotic pathways, making it a promising strategy in anticancer drug discovery. In the present study, a systematic computational approach integrating Quantitative Structure–Activity Relationship (QSAR) and molecular docking was employed to identify structural determinants of BCL-2 inhibition and to prioritize potential lead compounds. A curated dataset of BCL-2 inhibitors was standardized, and molecular descriptors were calculated using PaDEL and RDKit. After feature selection, models were built using Multiple Linear Regression (MLR), Random Forest (RF), and Support Vector Regression (SVR). The best-performing model demonstrated robust predictive power with high internal and external validation statistics (R² > 0.80, Q² > 0.75). Applicability domain analysis confirmed model reliability. Docking studies were performed using the crystal structure of BCL-2 (PDB ID: 4LVT) to validate binding interactions within the BH3 groove. Key interactions included hydrogen bonding with Asp108 and Arg146, and hydrophobic contacts with Phe101 and Tyr161, consistent with known BCL-2 pharmacophore features. Top-ranked compounds exhibited favorable docking scores (−9.2 to −11.5 kcal/mol) and satisfied drug-likeness and ADMET criteria. The integrated QSAR–docking workflow highlighted that balanced hydrophobicity, presence of hydrogen bond acceptors, and aromatic moieties are crucial for activity. This study provides valuable insights into the structural requirements for BCL-2 inhibition and offers a computational framework for designing novel anticancer agents with improved potency and pharmacokinetic profiles.