Machine Learning Applications for Drug Repurposing
Abstract
Machine learning (ML) is revolutionizing drug repurposing, offering a more efficient, cost-effective approach to drug discovery by identifying new therapeutic uses for existing drugs. ML algorithms process large, complex biomedical datasets, find hidden patterns that reveal unexpected links between drugs and diseases, and predict potential side effects. This advancement holds significant promise for precision medicine and personalized healthcare. This chapter aims to explore the growing role of ML in drug repurposing, an emergent frontier that aims to identify new therapeutic uses for existing drugs, thereby accelerating the pace of medical innovation while mitigating cost and risk. The chapter discusses various case studies, demonstrating the application of ML in identifying drug–disease connections and predicting adverse drug reactions, significantly contributing to precision medicine. In addition, the chapter investigates the successes and challenges encountered in this nascent field, highlighting the potential of ML to modernize drug discovery. Emphasis is placed on the ethical and privacy concerns surrounding the use of patient data in ML models, urging the need for robust regulations. This comprehensive review serves as a practical guide for those at the intersection of pharmaceutical research, clinical practice, and computer sciences, advocating for the synergetic use of these fields in advancing healthcare.
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