Journal
CURRENT PROTEIN & PEPTIDE SCIENCE
Volume 19, Issue 6, Pages 537-561Publisher
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1389203718666161108091609
Keywords
Drug-target interaction; machine learning; supervised method; semi-supervised method; drug repurposing; poly-pharmacology; similarity based method; feature based method; drug design
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Funding
- Department of Science and Technology (DST), Government of India, New Delhi under Women Scientist Scheme-A [SR/WOS/LS62/2013]
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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.
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