TY - JOUR
T1 - A survey of current trends in computational drug repositioning
AU - Li, Jiao
AU - Zheng, Si
AU - Chen, Bin
AU - Butte, Atul J.
AU - Swamidass, S. Joshua
AU - Lu, Zhiyong
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
KW - Chemical structure
KW - Computational drug repositioning
KW - Drug combination
KW - Genome
KW - Integrative strategies
KW - Phenome
KW - Prediction validation
UR - https://www.scopus.com/pages/publications/84960079810
U2 - 10.1093/bib/bbv020
DO - 10.1093/bib/bbv020
M3 - Article
C2 - 25832646
AN - SCOPUS:84960079810
SN - 1467-5463
VL - 17
SP - 2
EP - 12
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 1
ER -