Machine Learning in Drug Repurposing

Drug repurposing, also known as drug repositioning, is a strategy that involves finding new therapeutic uses for existing drugs. This approach has gained significant attention in recent years due to its potential to reduce the time and cost associated with drug development. The traditional drug discovery process is often lengthy, expensive, and fraught with high attrition rates. In contrast, drug repurposing leverages existing safety and pharmacokinetic data, thereby accelerating the development timeline and reducing associated risks. Artificial intelligence (AI) and machine learning (ML) algorithms, including deep learning and natural language processing, have demonstrated their utility in various stages of drug development. This chapter provides an in-depth analysis of the AI/ML methodologies employed in drug repurposing. Furthermore, we discuss the role of AI/ML in enhancing drug repurposing efforts, particularly in the context of emerging health threats, including cancer, COVID-19, and neurodegenerative diseases. Ultimately, this chapter offers insights into future perspectives and potential advancements in the field, emphasizing the importance of collaborative efforts and innovative solutions in addressing existing challenges.