An integrative machine learning and structure-driven drug repositioning strategy for human IRAK4-targeted cancer therapy.

), root mean square fluctuation (RMSF), principal component analysis (PCA), hydrogen bonding, and MMGBSA/MM-PBSA profiles. The efficacy of these candidates was further supported by their low toxicity profiles, via graph neural network (GNN)-based toxicity analysis. These findings establish a scalable computational strategy for identifying repurposable oncology therapeutics and propose udenafil, linagliptin, benflumetol, and nimodipine as mechanistically credible candidates for experimental validation.