Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Research Center for Pharmaceutical Nanotechnology and Department of Pharmaceutics, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
Comput Biol Med. 2019 Jun;109:254-262. doi: 10.1016/j.compbiomed.2019.05.006. Epub 2019 May 8.
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.
药物重定位或再定位，即引入现有药物的新应用，是药物发现领域的一个新兴领域。为了以成本效益和时间有效的方式提高研究和开发（R&D）过程的成功率，全球许多制药公司都进行了大量投资。此外，许多研究人员已经提出了各种方法和数据库，用于各种药物的重新定位。然而，目前还没有一个适当的、组织良好的数据库。为此，我们首次基于 DrugBank 和 KEGG 数据开发了一个名为“DrugR+”的新数据库。与 DrugBank 相比，我们开发的数据库具有一些优势，其界面为药物的单一和综合重定位提供了新的功能。此外，它还包含四个新的数据集，可用于使用有监督机器学习方法预测药物-靶标相互作用。作为一个案例研究，我们介绍了一些药物的新应用，并讨论了获得的结果。在补充文件中还报告了在生成的数据集上对几种机器学习方法的比较。由于包含了几个标准化表格，DrugR+被组织起来，为药物的重新定位和组合应用提供了有关数据结构的关键信息。它为专业用户提供了 SQL 查询功能，并为非专业用户提供了不同选项的适当方法。此外，DrugR+还包括接受一种药物并为某些用途提出潜在药物列表的重定位服务。总之，DrugR+是一个免费的基于网络的数据库，可以通过（http://www.drugr.ir）访问，并且可以通过映射-减少并行处理方法进行更新，以提供最相关的信息。
