Repositioning Drugs: A Computational Approach

Computational drug repositioning has emerged as an efficient approach to discovering new indications for existing drugs, offering lower risk and cost compared to traditional drug discovery methods. Various computational approaches have been developed, including targetbased, gene-expression-based, phenome-based, and multi-omics-based methods. Recent advancements leverage diverse data sources, such as biomedical databases and online healthrelated information. Techniques incorporating drug structure and target information have shown promising results in predicting new drug indications. Despite significant progress, challenges remain, including data noise reduction, method ensemble, negative sample selection, and data sparseness. Overall, computational drug repositioning continues to be a valuable tool in drug discovery and development.