Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.

ABSTRACT: Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate o

SUBMITTER: Peon A 

PROVIDER: S-EPMC5476590 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature