Bioinformatics-Driven Drug Discovery for Breast Cancer: A Focus on Natural Compounds

Breast cancer remains a leading cause of morbidity and mortality among women worldwide, with rising incidence projected to surpass three million new cases annually by 2040. Despite significant advances in targeted therapies such as endocrine treatment, HER2 inhibitors, PARP inhibitors, and CDK4/6 inhibitors, challenges persist in managing therapy resistance, heterogeneity across subtypes, and poor prognosis in triple-negative breast cancer (TNBC). Bioinformatics has emerged as a transformative tool in drug discovery, enabling high-throughput analyses of genomic, transcriptomic, and proteomic datasets for identifying molecular targets, predictive biomarkers, and potential repurposed drugs. This review explores the integration of computational approaches with natural compounds for breast cancer therapy, highlighting the significance of multi-target drug discovery, molecular docking, and AI-driven diagnostics. Natural products-including flavonoids, stilbenoids, lignans, and terpenoids-demonstrate promising anti-aromatase, anti-proliferative, and pathway-inhibiting properties with reduced toxicity. Moreover, artificial intelligence has accelerated progress in screening, diagnosis, staging, and treatment prediction, bridging gaps in precision medicine. Collectively, these insights emphasize the critical role of bioinformatics-driven strategies and natural compounds in developing safer, more effective therapies for breast cancer management.