Abstract

Ovarian and breast cancers are the most complex malignancies in women, showing high genetic heterogeneity, dynamic tumor microenvironments, and resistance to conventional therapies. Computer-Aided Drug Design has emerged as a transformative tool that could overcome these challenges by streamlining drug discovery, improving target specificity, and enabling personalized treatment approaches. Techniques such as molecular docking, pharmacophore modeling, and QSAR analysis have helped identify new inhibitors of key targets - HER2, BRCA1/2, PI3K/AKT/mTOR pathways. CADD is also instrumental in optimizing existing therapies, predicting mechanisms of resistance, and repurposing FDA-approved drugs for higher efficacy against cancer-specific pathways. Advances in nanotechnology, combined with CADD, have resulted in the creation of targeted nanocarriers like liposomes and polymeric micelles, allowing for improved delivery of drugs as well as decreasing systemic toxicity. Artificial intelligence and machine learning are currently accelerating the development of multi-targeted therapies and biomarkers towards precision medicine. Despite the present obstacles, tumor heterogeneity, and drug delivery barriers, such continued innovations within CADD technology and experimental validation may revolutionize ovarian and breast cancer treatments towards a more personalized and sustainable therapeutical treatment strategy.

Keywords

ovarian cancer, breast cancer, computer-aided drug design (CADD), molecular docking, targeted therapies, nanocarriers