Ordinary and advance search capabilities are attainable.



Combined drug replacement using SQL statments are accessible.



This section includes several datasets for drug repurposing applications.



Entity relations diagram and DDL are available.






Search strategies
The web interface of Drug R+ provides an easy way for searching all of the existing data. In the search section, two strategies are available for both unprofessional and professional users: i) Unprofessional users can select their tables, and then can confine search space using a proper way. They can also export the acquired results into an excel file. ii) Professional users can express their queries in the specified box. In this section, all of the SQL queries like group by and nested queries, and many other queries, which are relevant to information retrieval, can be written. However, data manipulating operations such as update and delete are not permitted. Like the first search strategy, professional users can also export their favorable results into an excel file.

As we know, the current data include very important latent information which can be obtained by data-mining methods. In addition to the professional search strategy, CDR, which searches existing data and mines latent information, is another brilliant capability of Drug R+. For an entered drug, this section suggests list of several drugs if they are available. The proposed list can be used in two ways. One or more drugs of the suggested list can be nominated for combining or replacing with the selected drug in order to reach combination therapy or CDR in the first and second ways respectively. In using this capability, a user can limit mining operations based FDA approved, FDA not approved, or both of them. Furthermore, a user must select target types which are divided into enzymes, carrier proteins, transporter proteins, and other types of proteins.

The web interface of Drug R+ contains the mined datasets which have been categorized into four classes, including: ENZ, ICP, GPCR, and NRP. For every category, a distinct text file, which contains known drug-target interactions, has been generated. Besides known drug-target interactions, another text file, which consists of unknown drug-target interactions, has been produced for every class of drug-target interactions. In obtaining the datasets, pseudo code introduced in fig.5 has been employed. After creating the eight databases, handheld and automatic investigation of data have been done, and then data with missing values have been eliminated. Using known drug-target interactions, a model can be built and tested by machine learning methods. After that, the model is applied to unknown drug-target interaction dataset in order to predict interactions.

Please cite us as
Masoudi-Sobhanzadeh, Y., Omidi, Y., Amanlou, M., & Masoudi-Nejad, A. (2019). DrugR+: A comprehensive relational database for drug repurposing, combination therapy, and replacement therapy. Computers in Biology and Medicine.

Laboratory of systems biology and bioinformatics (L.B.B). update: 2019:11