Navigating the Computational Landscape for Drug Repurposing

Giving old drugs new uses, a process known as drug repurposing, is an attractive strategy for finding therapeutic candidates for a wide number of diseases. In this context, data-driven approaches have emerged as a suitable framework to target this challenge. From molecular docking and network-based methods to omics data integration, computational techniques give invaluable insights into drug repurposing research. In the present review, we describe these methodologies and knowledge-based resources, also emphasizing the new horizons that artificial intelligence and large language models are revealing. A set of case studies illuminate the practical applications of these computational approaches to the identification of repurposing opportunities. By addressing a set of key challenges and proposing future directions, this review aims to be a resource for researchers navigating the multifaceted landscape of computational drug repurposing.