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.
Collects data about therapeutic protein and nucleic acid targets. Therapeutic Target Database focuses on information about drug resistance mutations, gene expressions and target combinations data for the targets and drugs. The database is organized through five main panels which authorizes to browse it by advanced search, patient data, targets or drugs groups or by model data. It also permits to downloads various datasets.
Gathers information about different types of genes. GeneOrienteer is an online repository assisting users in the identification of protein-gene interactions. This database using an algorithm that maps orthologous genes. It contains several functions: identical anatomical expression, phenotype, function annotation (e.g., biological process in gene ontology (GO)), microarray co-expression, and the presence of interlogs.
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.
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.
Includes information on 5153 putative drug–target pairs for 150 human pathogens derived from available drug–target crystallographic complexes. The TIPs database has been developed with the aim of facilitating the identification of new therapeutic targets in organisms responsible for human infections. All entries are thoroughly annotated with both sequence and functional information. The database can be queried by organism name, protein family or function. The query returns a sortable table providing information about both known and predicted drug–target pairs and links to visualize specific information on the drug(s) physicochemical properties, structure, indication and side effects, the target(s) and to visually analyse or download their 3D complexes.
Consolidates disparate data sources describing drug-gene interactions and gene druggability. DGIdb provides an online platform for finding several types of data. This resource organizes and shows drug-gene interactions and gene druggability information from web resources, papers or other databases.
Gathers information about the identities of mammalian proteins. TPDB is an online repository that attempts to provide a comprehensive, up-to-date listing of reported target proteins. It informs users about over 260 distinct proteins targeted by one or more of 20 small molecules or their metabolites. The users can query data by chemical, target tissue, species, protein name, or combinations of these criteria.
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.
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.
Integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present, the updated database contains >6,000 target proteins, which are annotated with >330,000 relations to 196,000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range.
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.
Allows to identify and prioritize molecular targets for drug development. TDR focuses on pathogens responsible for neglected human diseases. The database includes pathogen specific genomic information with functional data for genes collected from various sources, including literature curation. The interface allows to combine, save, export and share several query results.
Displays a comprehensive network for exploring the connection between angiogenesis and diseases at multilevels including protein-protein interaction, drug-target, disease-gene and signaling pathways among various cells and animal models recorded through text-mining. To enlarge the scope of the PubAngioGen application, the database also links to other common resources including STRING, DrugBank and OMIM databases, which will facilitate understanding the underlying molecular mechanisms of angiogenesis and drug development in clinical therapy.
Gathers data about drugs, their targets and associated ortholog predictions for over 640 eukaryotic species. ECOdrug is composed of two main panels: (i) the drug panel displays related information about drugs such as its target conservation in taxonomic group and species and; (ii) the drug target panel provides features including a graphic display of drug target conservation and a search functionality that can be queried by HGNC symbols, ensembl gene IDs or ensembl peptide IDs.
Compiles information about yeast chemogenomic screening data. NetwoRx integrates three larges chemogenomic experiments, covering nearly 6000 yeast genes and about 460 drugs. Moreover, the database includes pathways and phenotypes targeted by drugs, computes drug–drug similarity metrics for mode of action analysis and build drug–phenotype networks extracted from fours genes sets collections. Users can also query new gene lists against the entire collection of drug profiles to retrieve the drugs that target them.
Facilitates identification and prioritization of candidate targets suitable for new drug development projects. Target-Pathogen is an online resource that allows genome wide based target ranking and identification. This resource can help users to computationally apply a set of filters to obtain a short list of proteins that fulfils user predefined criteria such as protein function, metabolic role, off-targeting, structural drugg ability, essentiality and omic experiments.
Provides comprehensive information about miRNAs affecting drug therapies. mTD is a database that provides the sentences describing the drug-miRNA interactions with links to corresponding publications for each drugmiRNA association. This resource can help to understand the mechanisms underlying drug actions better and design more efficient drug combinations, like the combination of miRNA inhibitors and drugs.
Provides access to a comprehensive set of putative and native protein-drug interactions in the structural human proteome. The structural human proteome includes about 10,000 human and human-like (with high sequence similarity to human proteins) proteins with known 3-D structures. The database includes data for popular, FDA-approved drugs. The corresponding protein-drug interactions were generated with three predictors, and were collected from and linked with three related databases of known protein-drug interactions.
Consists of a resource to propose drug therapies from genome-wide experimental results, including variant and gene lists. PanDrugs includes about 9000 drugs, 4800 unique genes, and more than 43,900 direct and non-redundant gene-drug interactions. It represents a drug prescription tool proposing cancer therapies with a rationale based on pathway context, collective gene impact, and information provided by functional experiments.
Offers access to drug-labeling data and facilitates their use in regulatory science, drug development, scientific research, and clinical application. FDALabel contains over 95 000 full-text Structured Product Labelings (SPLs) that include human prescription drugs and biological products, and human over-the-counter (OTC) drugs. Users can employ the database to get access to drug indications and warnings, as a pharmaceutical company for drug development, or as a researcher studying drug safety.