Computational techniques to study breast cancer scaffolds for antiangiogenesis: a review.

CONTEXT: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Tumor angiogenesis plays a crucial role in breast cancer progression, making angiogenesis-associated pathways attractive therapeutic targets. Computational drug discovery approaches, including virtual high-throughput screening (VHTS), molecular docking, molecular dynamics simulations, and binding free energy calculations, have emerged as valuable tools for identifying and optimizing anticancer compounds. This review evaluates the application of these computational techniques in the discovery of antiangiogenic therapeutic candidates for breast cancer.

METHODS: A systematic literature search was conducted across Google Scholar, PubMed, ScienceDirect, and The Cancer Genome Atlas (TCGA). Following screening and eligibility assessment according to predefined inclusion criteria, 38 studies published between 2015 and 2025 were included in the qualitative synthesis. The selected studies investigated computational approaches targeting angiogenesis-related pathways, including VEGFR-2, STAT3, HER2, PI3K/Akt, and SphK1 signaling.

RESULTS AND CONCLUSIONS: The reviewed studies demonstrated that VHTS, molecular docking, ADMET prediction, molecular dynamics simulations, MM-GBSA/MM-PBSA analyses, and QSAR modeling effectively facilitate hit identification and lead optimization. Several compounds, including triazolopyrazine derivatives, curcumin, liquiritin, and novel STAT3 inhibitors, exhibited promising antiangiogenic activity and favorable binding characteristics, highlighting the potential of computational approaches in advancing breast cancer drug discovery.