Welcome to the DrugMiner
Welcome to the DrugMiner (drug target database) homepage.
First step in drug design and discovery is to find that which interactions can occur between one drug and one protein. It is really crucial to know whether one protein can interact with one drug or not. Since experimental approaches in identification of druggable proteins as well as drug target prediction are time consuming survey and needs high cost of experiments, an effective way to decide if one protein can interact with drugs is crucial. Therefore, computational approaches due to their high throughput outcomes, can be effectively used beside experimental methods to reduce cost of researches in terms of time and financial. DrugMiner is an online database and motor for drug target prediction and functional annotations. All the targets were predicted by a machine learning algorithm, DrugMiner, which was developed by analyzing thousands of Drug-Protein interactions. Common features associated with Drug-Target interaction have been identified and used to predict drug targets. DrugMiner hosts predicted drug targets in human. As a recent version, users may provide their own protein Uniprot ID, protein name or protein sequence to customized target prediction. This is the first version of the database. We look forward to your feedback.
If you make use of the data presented here, please cite the following article in addition to the primary data sources:
Jamali, A.A., Ferdousi, R., Razzaghi, S., Li, J., Safdari, R., Ebrahimie, E., DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins, Drug Discovery Today (2016), 21 (5), 718-724. http://dx.doi.org/10.1016/j.drudis.2016.01.007