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Drug repurposing or repositioning, which introduces new applications of the existing drugs, is an emerging field in drug discovery scope. To enhance the success rate of the research and development (R&D) process in a cost- and time-effective manner, a number of pharmaceutical companies worldwide have made tremendous investments. Besides, many researchers have proposed various methods and databases for the repurposing of various drugs. However, there is not a proper and well-organized database available. To this end, for the first time, we developed a new database based on DrugBank and KEGG data, which is named "DrugR+". Our developed database provides some advantages relative to the DrugBank, and its interface supplies new capabilities for both single and synthetic repositioning of drugs. Moreover, it includes four new datasets which can be used for predicting drug-target interactions using supervised machine learning methods. As a case study, we introduced novel applications of some drugs and discussed the obtained results. A comparison of several machine learning methods on the generated datasets has also been reported in the Supplementary File. Having included several normalized tables, DrugR + has been organized to provide key information on data structures for the repurposing and combining applications of drugs. It provides the SQL query capability for professional users and an appropriate method with different options for unprofessional users. Additionally, DrugR + consists of repurposing service that accepts a drug and proposes a list of potential drugs for some usages. Taken all, DrugR+ is a free web-based database and accessible using (http://www.drugr.ir), which can be updated through a map-reduce parallel processing method to provide the most relevant information.
This article was published in the following journal.
Name: Computers in biology and medicine
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