DefinePK

Abstract

Computational drug design has transformed pharmaceutical research by streamlining the discovery and development of innovative chemotherapy agents. In the context of breast cancer (BC), advanced computational techniques—such as molecular docking, virtual screening, and pharmacophore modeling—have significantly contributed to the identification of promising drug candidates. Both structure-based and ligand-based drug design strategies have enabled precise targeting of oncogenic proteins, including estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), BRCA1/BRCA2, and vascular endothelial growth factor (VEGF). The integration of artificial intelligence (AI) and machine learning (ML) has further enhanced predictive modeling, improving drug efficacy, optimizing lead compound selection, and reducing development timelines. AI-driven approaches, particularly deep learning and neural networks, have improved the prediction of binding affinities, selectivity, and potential off-target effects in ligand-based drug design. These methodologies have accelerated the discovery of novel therapeutic agents by efficiently analyzing extensive datasets and virtual screening outcomes. Numerous in silico-identified compounds with strong binding affinities have progressed to clinical evaluation. This review provides a comprehensive overview of computational strategies in BC drug discovery, highlighting key methodologies, emerging molecular targets, the impact of AI in drug design, and the translational challenges involved. By harnessing these computational tools, researchers can enhance precision, reduce costs, and accelerate the development of targeted therapies for breast cancer.