Machine Learning Approaches in Predicting Cancer Drug Response

Cancer treatment outcomes vary widely among patients due to tumour heterogeneity, genetic diversity, and environmental factors. Predicting drug response accurately is a central challenge in precision oncology. Machine learning (ML) has emerged as a powerful tool to integrate multi-omics data and clinical information to forecast therapeutic responses. This paper provides a comprehensive and in-depth analysis of machine learning approaches used in predicting cancer drug response. It discusses data sources, preprocessing strategies, feature engineering, algorithmic models, validation techniques, and real-world applications. The study also highlights challenges such as data imbalance, interpretability, and reproducibility, and explores emerging directions including explainable AI, federated learning, and digital twin models. The integration of ML into oncology is expected to revolutionize personalized medicine, improve treatment efficacy, and reduce adverse effects.

Machine Learning; Cancer Drug Response; Precision Oncology; Deep Learning; Multi-omics Data; Drug Resistance; Artificial Intelligence; Predictive Modeling.

1. Schneider, G. (2018). Automating drug discovery. Nature Reviews Drug Discovery. [Google Scholar] [Crossref]

2. Vamathevan, J., et al. (2019). Machine learning in drug discovery. Nature Reviews Drug Discovery. [Google Scholar] [Crossref]

3. Chen, H., et al. (2018). Deep learning in drug discovery. Drug Discovery Today. [Google Scholar] [Crossref]

4. Zhavoronkov, A., et al. (2019). AI in drug discovery. Nature Biotechnology. [Google Scholar] [Crossref]

5. Costello, J. C., et al. (2014). Prediction models in pharmacogenomics. Nature Biotechnology. [Google Scholar] [Crossref]

6. Kourou, K., et al. (2015). ML in cancer prognosis. Computational Biology Journal. [Google Scholar] [Crossref]

7. Libbrecht, M., & Noble, W. (2015). ML in genomics. Nature Reviews Genetics. [Google Scholar] [Crossref]

8. Su, R., et al. (2020). Deep learning for cancer therapy. Briefings in Bioinformatics. [Google Scholar] [Crossref]

9. Ali, M., et al. (2018). Drug response prediction models. Bioinformatics. [Google Scholar] [Crossref]

10. Gorgulla, C., et al. (2020). Virtual screening platforms. Nature. [Google Scholar] [Crossref]

11. Ramsundar, B., et al. (2019). Deep learning life sciences. O’Reilly. [Google Scholar] [Crossref]

12. Ekins, S., et al. (2020). AI in pharma. Nature Materials. [Google Scholar] [Crossref]