Abstract
Drug repurposing, or repositioning, is the process of identifying new uses for existing drugs. Machine learning (ML) has emerged as a powerful tool in drug repurposing, as it can analyze large-scale biological, chemical, and clinical data to predict potential drug-disease relationships. This article explores the role of machine learning in drug repurposing, focusing on its applications in identifying candidate drugs for new indications, optimizing drug screening processes, and predicting drug toxicity. We also discuss the challenges and future directions of using machine learning in drug repurposing, highlighting its potential to accelerate drug discovery and improve therapeutic outcomes..
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