A survey of current trends in computational drug repositioning

• Published in: Briefings in bioinformatics Vol. 17; no. 1; pp. 2 - 12
• Main Authors: Li, Jiao, Zheng, Si, Chen, Bin, Butte, Atul J., Swamidass, S. Joshua, Lu, Zhiyong
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.01.2016; Oxford University Press
• Subjects: Bioinformatics; Computational Biology - trends; Current Progress in Bioinformatics 2016 Papers; Data Mining; Data processing; Drug Combinations; Drug Repositioning - statistics & numerical data; Drug Repositioning - trends; drugs; Experimental methods; Genomics; Genotype & phenotype; Humans; information processing; Machine Learning; Molecular Structure; neoplasms; Phenotype; precision medicine; Prescription drugs; Repositioning; surveys; Surveys and Questionnaires; genome; phenome; drug combination; prediction validation; computational drug repositioning; chemical structure; integrative strategies
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbv020

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.