Structure-Based Virtual Screening in Oncology: Repurposing FDAApproved Drugs for Breast Cancer

Structure-based virtual screening (SBVS) has been recently established as a significant and fruitful computational method in drug repurposing, where drugs already approved by the FDA are screened for their potential therapeutic effects against breast cancer. This review lays down the principles of SBVS while broaching important computational programs such as molecular docking software, molecular dynamic simulation, and AI-driven predictive models. Databases of drug repositioning such as DrugBank, PubChem, and ChEMBL provide comprehensive datasets mandatory for virtual screening. The past few years have seen SBVS emerge as a tool for drug repurposing with exciting new candidates. Some of these are metformin, statins, NSAIDs, beta-blockers, and antidepressants, with anti-cancer activities reported through a plethora of mechanisms. However, accuracy limits, experimental validation, and regulatory hurdles impede clinical translation. The way forward envisions approaches integrating AI, multi-target drug strategies, and personalized medicine to enhance SBVS's suitability and reliability in oncology. Utilizing computational methods and an inter-disciplinary dynamic, drug repurposing with SBVS is anticipated to get very far and create a viable toolkit for breast cancer therapeutic discovery.