Advanced Artificial Intelligence and data science in bioinformatics-driven drug discovery for cancer: Pathways toward shorter and less toxic treatment

Cancer remains one of the leading causes of death worldwide, with the GLOBOCAN estimates placing the 2022 global burden at close to 20 million new cases and 9.7 million deaths (Bray et al., 2024), a burden projected by the American Cancer Society (2024) to rise to roughly 35 million annual cases by 2050. Conventional cytotoxic chemotherapy, though still central to treatment for many tumor types, is frequently associated with prolonged treatment courses, non-specific systemic toxicity, and reduced quality of life. This review synthesizes recent literature on the application of artificial intelligence (AI) and data science within bioinformatics-driven cancer drug discovery, examining how these tools are reshaping target identification, molecular design, biomarker discovery, and treatment personalization. The analysis shows that deep learning-based protein structure prediction (Jumper et al., 2021), generative molecular design (Gangwal & Lavecchia, 2024), multi-omics target identification (Bhinder et al., 2021; Wei et al., 2023), digital pathology and radiomics (Bera et al., 2022; Lu et al., 2024), and machine learning models for predicting chemotherapy toxicity (Huang et al., 2024; Moslemi et al., 2025) are collectively shortening discovery timelines, improving the precision of treatment selection, and reducing treatment-related adverse effects in reported studies. Case evidence is presented, including a generative-AI-designed molecule that reached Phase I clinical trials in under 30 months (Insilico Medicine, 2022) and the 2024 Nobel Prize in Chemistry awarded for the AlphaFold protein-structure-prediction system. While these advances present a credible pathway toward shorter, more targeted, and less toxic cancer treatment, and in specific molecular contexts may reduce reliance on conventional chemotherapy, the evidence does not yet support claims that AI will universally eliminate chemotherapy; rather, it points toward an increasingly personalized standard of oncologic care. The review concludes by discussing the ethical, regulatory, and data-governance barriers that must be addressed for these gains to be realized safely and equitably.