Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework.

ABSTRACT:

Motivation

In silico prediction of drug-target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently.

Results

In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug-target interaction networks, and show that drug-target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug-target interactions from chemical, genomic and pharmacological data on a large scale. The proposed me

SUBMITTER: Yamanishi Y 

PROVIDER: S-EPMC2881361 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature