A computational framework to integrate complex relationship among different types of data and infer the potential drug targets by using the semantic web technology, and to improve performance through network neighborhood effect modeling. As a preliminary research, an OWL ontology including drugs, diseases, genes, pathways, SNPs, and their relations from the PharmGKB were constructed.
A collection of drug and small molecule related gene sets based on quantitative inhibition and/or drug-induced gene expression changes data. DSigDB allows users to search, view, and download drugs/compounds and gene sets. DSigDB gene sets provide seamless integration to GSEA software for linking gene expressions with drugs/compounds for drug repurposing and translational research.
Predicts new uses for existing compounds. Project Rephetio Browser uses machine learning to systematically learn network patterns of drug efficacy. This method translates the network paths between a compound and disease into a predicted probability of treatment. It makes predictions for 1538 approved small molecule compounds and 136 complex diseases, resulting in a total of 209168 compound-disease pairs. Predictions are created from Hetionet v1.0, an integrative network of biomedicine that contains 2250197 relationships of 24 types. Data about compounds (identifiers, names, and desciptions) are from DrugBank, while diseases are from the Disease Ontology.
An integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47031 nodes of 11 types and 2250197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Hetionet was created for Project Rephetio, which aims to systematically identify why drugs work and predict new therapies for drugs. Hetionet network is accessible via a Neo4j Browser and is available for download in three formats: JSON, Neo4j and TSV.
Aims to discover new indications for existing drugs with known safety profiles. Drug Repurposing Hub is an interactive website designed to rapidly identify drugs for evaluation in disease models. The Hub consists of (i) a physical drug screening library available in multiple plate formats at the Broad Compound Management facility, (ii) manually curated annotations as part of a comprehensive publicly-accessible information resource with data API, and (iii) experimental results with liquid chromatography-mass spectrometry (LC-MS) tracings and future cellular assays.
A database of 3D chemical structures of drugs, providing several collections of ready-to-screen SD files of drugs fragments. The structure of each drug has been manually checked to assign the exact stereochemistry to chiral centers. e-Drug3D also provides a link to the FDA website for each registered molecule and gives several informations such physicochemical properties, synthesis references, commercial suppliers…