Machine Learning in Drug Repurposing

This chapter explores the transformative role of machine learning in accelerating drug repurposing by leveraging predictive knowledge systems to identify new therapeutic uses for existing compounds. It outlines key computational strategies, including network-based modeling, and deep learning approaches in various aspects of target-based, feature-based, and disease-centric based approach of drug repurposing. Emphasis is placed on integrating diverse biomedical data sources to enhance model accuracy and translational potential. The chapter further discusses recent advancements in data-driven frameworks, challenges related to data quality and interpretability, and emerging trends such as explainable AI and multimodal learning. By examining these innovations, this chapter highlights how predictive analytics can streamline discovery pipelines, reduce development costs, and promote precision medicine in modern pharmacology.