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.
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