• Deep learning models achieve superior predictive accuracy (AUC 0.92) for drug sensitivity in cancer cell lines compared to traditional methods.
• Interpretability analysis reveals key biomarkers and pathways associated with drug resistance, offering potential therapeutic targets.
• The proposed framework integrates genomic and drug response data, enabling robust predictions across diverse cancer types.
• The study provides a comprehensive benchmark and open-source code for reproducible research in precision oncology.