An integrated database resource consisting of 16 main databases, which are categorized into systems, genomic, chemical and health information. The PATHWAY, BRITE and MODULE databases in the systems information category contain KEGG pathway maps, BRITE hierarchy and table files and KEGG modules, respectively, as representations of high-level functions. They are all manually created based on published literature. The genomic information category contains the GENOME and GENES databases for collections of organisms with complete genomes and their gene catalogs, which are mostly taken from RefSeq and GenBank databases. The COMPOUND, GLYCAN, REACTION, RPAIR, RCLASS and ENZYME databases in the chemical information category contain chemical substances and reactions and are collectively called KEGG LIGAND for historical reasons. The health information category consists of the DISEASE, DRUG, DGROUP and ENVIRON databases for disease and drug information.
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.
An interactive, visual database containing more than 618 small molecule pathways found in humans. More than 70% of these pathways (>433) are not found in any other pathway database. SMPDB is designed specifically to support pathway elucidation and pathway discovery in metabolomics, transcriptomics, proteomics and systems biology. It is able to do so, in part, by providing exquisitely detailed, fully searchable, hyperlinked diagrams of human metabolic pathways, metabolic disease pathways, metabolite signaling pathways and drug-action pathways.
Detects mutual drug impairments and helps to appraise the drug response. Transformer is a comprehensive resource that combines scientific information on phase I and phase II reactions of enzymes, transporter enzymes, prodrugs, food and herbs that increase risk of adverse drug reactions. This resource can be a sound starting point for further research in drug interactions.
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.
Gathers network pharmacology related interactions information at the systemic level. PhID aims to provides a repository for visualizing relationships between entities such as drugs, targets, diseases, genes, pathways, and side-effects. The database includes more than 306000 activities. Searches can be made by names, ids, molecular structures, or molecular fragments for different purposes.
A database of drug-induced pathways, which was generated by KEGG pathway enrichment analysis for drug-induced upregulated genes and downregulated genes based on drug-induced gene expression datasets in Connectivity Map. Drug-Path provides user-friendly interfaces to retrieve, visualize and download the drug-induced pathway data in the database. In addition, the genes deregulated by a given drug are highlighted in the pathways. All data were organized using SQLite. The web site was implemented using Django, a Python web framework.
Supports the mining of scientific literature and enables the reconstruction of recently documented antimicrobial combinations. Antimicrobial Combination Networks contains data on antimicrobial combinations that have been experimentally tested against Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, Listeria monocytogenes and Candida albicans, which are prominent pathogenic organisms and are well-known for their wide and growing resistance to conventional antimicrobials. Researchers are able to explore the experimental results for a single organism or across organisms.
Presents disease relationships from the viewpoint of gene regulation mechanism. DNetDB records common dysfunctional regulation mechanism, common disease-related genes and drugs of disease pairs. In this way, DNetDB has the capability to support pathogenesis exploration, drug repositioning and drug development. It provides an easy-to-use web interface to search and browse disease relationships through disease name(s), disease gene, drug, GSE ID(s), and tissue (s). DNetDB facilitates the study of disease relationships and provides insightful clues to investigate diseases’ etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.
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