Drug repurposing software tools | Drug discovery data analysis
Drug repositioning has been regarded as one of the most promising strategies for translational medicine. Common efforts to find new uses for existing drugs depend on text mining, chemical genetics and network analysis. In the April 2012 issue, proposed the use of genome-wide association studies (GWAS) for drug repositioning.
Allows to analyze the Mode of Action (MoA) of novel drugs and to identify known and approved candidates for “drug repositioning”. MANTRA is a computational tool based on network theory and non-parametric statistics on gene expression data, which offers three “workspaces”: Analysis, Network and Search. Users can visually explore the Drug Network that provides, for each of the drugs, information about biochemical interactions, therapeutic indications, known MoA, pharmacology and targeted proteins.
Constructs target prioritization and drug repositioning hypotheses that are supported by genetics evidence. GCMap employs the genetic evidence to increase the chances of success of drug discovery programs. It is useful for genome wide association study (GWAS)-based drug repositioning. This tool builds a large number of statistically significant results and validation of these hypotheses would require extensive experimental work.
A package developed for the large-scale analysis of gene expression signatures. GeneExpressionSignature implements two rank-merging algorithms and two similarity-scoring algorithms. It provides a flexible solution for gene expression signature-based studies and holds great potential in biomedical research applications, such as drug repurposing. All of the functions in the GeneExpressionSignature package, except getRLs, support ratio, log-ratio, and rank data stored as assay data in the ‘‘ExpressionSet’’ object of the Biobase package as input data. The label of each column, as well as phenotypic data in the ‘‘ExpressionSet’’ object, is the biological state descriptions of the gene expression profiles.
Predicts many kinds of biological activity for compounds from different chemical series based on their 2D structural formulas. PASS finds new targets mechanisms for some ligands. It can reveal new ligands for some biological targets. The tool can be used to analyze the occurrence, in a database, of compounds predicted to be active for a well-defined set of PASS activities.