Machine Learning Integration in Next-Generation Chemotherapy Design: Challenges, Precision Medicine and Patient-Centered Impact

Machine learning (ML) has emerged as a transformative technology in oncology, offering significant promise for advancing chemotherapy design and individualized treatment strategies. By leveraging large-scale biological, genomic, and clinical datasets, ML models can accelerate drug discovery, predict therapeutic responses, and improve toxicity management. The integration of ML enhances precision medicine approaches and supports more patient-centered cancer care. However, challenges such as data scarcity, poor model interpretability, algorithmic bias, and limited cross-disciplinary collaboration hinder its full clinical adoption. This paper explores the current applications of ML in chemotherapy development, discusses the challenges in its integration, and highlights the potential impact on precision oncology and patient quality of life.