A survey of current trends in computational drug repositioning

By Jiao Li, Si Zheng, Bin Chen, Atul J. Butte, S. Joshua Swamidass, Zhiyong Lu. 2016

476 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

A review of validation strategies for computational drug repositioning (2016, methodology): “• There are currently three predominate validation methods used for computational repositioning studies: (1) case studies, (2) overlap of predictions with known drug indications and (3) sensitivity- or specificitybased methods.”
Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task (2016, background): “Developing a drug takes time and money: on average, around 14 years and $2 billion or more (3).”