Abstract
Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined.
Keywords: Drug-target interaction, machine learning, supervised method, semi-supervised method, drug repurposing, polypharmacology, similarity based method, feature based method, drug design.
Graphical Abstract
Related Journals
Protein & Peptide Letters
Related Books
Frontiers in Protein and Peptide Sciences
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