A Review of Network-Based Approaches for Anti-Cancer Drug Repurposing

Cancer drug repurposing leverages network medicine to identify new therapeutic uses for existing compounds, circumventing prohibitive development costs. This review examines recent advances in computational approaches that integrate network proximity and diffusion algorithms, transcriptomic signature matching through resources such as the Connectivity Map, graph neural networks including DT۲Vec+, and multi-omics integration frameworks. Applications across breast, cervical, and other malignancies reveal both shared vulnerabilities and subtype-specific opportunities for therapeutic intervention. Despite promising progress, critical challenges persist—including a validation bottleneck that separates in silico predictions from experimental confirmation and inherent trade-offs between prediction accuracy and biological interpretability. Future advancements will require hybrid methodological frameworks, standardized benchmarking protocols, and incorporation of structural dynamics to enhance translational impact in oncology.

کلیدواژه ها:

Drug Repurposing ، Multi-omics integration ، Computational Drug Discovery ، Data Mining ، Network Medicine