There are at least two good reasons for the on-going interest in drug-target interactions: first, drug-effects can only be fully understood by considering a complex network of interactions to multiple targets (so-called off-target effects) including metabolic and signaling pathways; second, it is crucial to consider drug-target-pathway relations for the identification of novel targets for drug development.
Gathers detailed drug, drug-target, drug action and drug interaction information about drugs. DrugBank is a web resource that contains information about FDA-approved drugs as well as experimental drugs going through the FDA approval process. The database also includes pharmaco-omic data covering the influence of drugs on metabolite levels, gene expression levels and protein expression levels, as well as data on investigational drug clinical trials and drug repurposing trials, and thousands of up-to-date drug images of approved drugs.
A resource for protein-chemical interactions. MATADOR differs from other resources such as DrugBank in its inclusion of as many direct and indirect interactions as we could find. In contrast, DrugBank usually contains only the main mode of interaction. The manually annotated list of direct (binding) and indirect interactions between proteins and chemicals was assembled by automated text-mining followed by manual curation. Each interaction contains links to PubMed abstracts or OMIM entries that were used to deduce the interaction.
A resource to explore known and predicted interactions of chemicals and proteins. Chemicals are linked to other chemicals and proteins by evidence derived from experiments, databases and the literature. STITCH contains interactions for between 300,000 small molecules and 2.6 million proteins from 1133 organisms. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously.
An integrated resource, focused on high-quality subsets from several bioactivity databases, which are aggregated and presented in a uniform manner, suitable for the study of the relationships between small molecules and targets.
A comprehensive knowledgebase for drug-target relationships related to cancer as well as for supporting information or experimental data. CancerResource contains about 91 000 drug-target relations, more than 2000 cancer cell lines and drug sensitivity data for about 50 000 drugs. CancerResource enables the capability of uploading external expression and mutation data and comparing them to the database's cell lines. Target genes and compounds are projected onto cancer-related pathways to get a better overview about how drug-target interactions benefit the treatment of cancer. Features like cellular fingerprints comprising of mutations, expression values and drug-sensitivity data can promote the understanding of genotype to drug sensitivity associations. Ultimately, these profiles can also be used to determine the most effective drug treatment for a cancer cell line most similar to a patient's tumor cells.
Contains a comprehensive collection of approved drugs in Japan, USA and Europe unified based on chemical structures and/or chemical components. KEGG DRUG is a database which contains information about molecular networks, such as targets, metabolizing enzymes and drug–drug interactions. All the marketed drugs in Japan, the prescription drugs but also the over-the-counter (OTC) drugs, are represented in the database, including crude drugs and Traditional Chinese Medicine (TCM) drugs.
Provides a database established for sharing active and inactive compounds from both PubChem and ChEMBL. ExCAPE-DB is a searchable open access database that serves as a data hub for giving researchers around world easy access to a publicly available standardized chemogenomics dataset, with the data and accompanying software available under open licenses. This dataset can be used as a comprehensive benchmark set to evaluate the performance of various machine-learning algorithms in the ExCAPE project.