Premium
Large‐scale prediction of drug–target relationships
Author(s) -
Kuhn Michael,
Campillos Mónica,
González Paula,
Jensen Lars Juhl,
Bork Peer
Publication year - 2008
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/j.febslet.2008.02.024
Subject(s) - context (archaeology) , computational biology , computer science , similarity (geometry) , data integration , function (biology) , systems biology , scale (ratio) , data science , drug target , drug discovery , biology , bioinformatics , data mining , artificial intelligence , genetics , physics , quantum mechanics , paleontology , image (mathematics) , pharmacology
The rapidly increasing amount of publicly available knowledge in biology and chemistry enables scientists to revisit many open problems by the systematic integration and analysis of heterogeneous novel data. The integration of relevant data does not only allow analyses at the network level, but also provides a more global view on drug–target relations. Here we review recent attempts to apply large‐scale computational analyses to predict novel interactions of drugs and targets from molecular and cellular features. In this context, we quantify the family‐dependent probability of two proteins to bind the same ligand as function of their sequence similarity. We finally discuss how phenotypic data could help to expand our understanding of the complex mechanisms of drug action.