Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data.

ABSTRACT: Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function

SUBMITTER: Wang Y 

PROVIDER: S-EPMC3823875 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature