A review of computational drug repurposing

Abstract

Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, 'drug repurposing' (or 'drug repositioning') has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.

Keywords: Computational drug repurposing; Deep learning; Drug repositioning; Machine learning; Text mining.

Figure 1. Conceptual diagram of de novo drug development and drug repurposing; the arrow denotes the initiation time in each scenario and the number denotes a period of time in years. In this figure, the objective of drug repurposing was assumed to be to identify or discover new targets for a drug marketed. Note that drug repurposing begins with target discovery for an existing drug, directly followed by phase 2 and 3 clinical trials, while animal and phase 1 clinical studies were not conducted as results for these studies are already available for an existing drug.