Drug-drug interaction databases | Chemical informatics data analysis
Drug–drug interaction (DDI) is a situation when one drug increases or decreases the effect of another drug. Information about DDIs is crucial for drug administration to avoid adverse drug reactions or therapeutic failure. For example, a recent study reports that DDIs are a significant cause of hospital admissions.
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.
Stores assertions about drug mechanisms and pharmacokinetic drug-drug interactions. DIKB is a system designed for predicting drug-drug interactions (DDIs) using drug mechanisms. The database contains assertions about specific entities such as drugs, drug metabolites, and enzymes. It enables knowledge-base curators to link each assertion about a drug property to both supporting and refuting evidence.
Provides integrated drug combinations from various data sources. DrugCombDB is a comprehensive database that offers 1) high-throughput screening assays of drug combinations, 2) external databases, and 3) manual curations from PubMed literature. In total, this resource includes almost 1,128,000 experimental data points with quantitative dose response and concentrations of drug combinations covering more of 560 unique drugs and a hundred of human cancer cell lines.
A database to revolve around the protein structure of catalytic PDE domains and the way PDE inhibitors can interact with them. PDEStrIAn is based on the underlying systematic and consistent protocol. With PDEStrIAn, it is possible to compare the interaction patterns of PDE-inhibitors to each other to, for example, identify crucial interactions determining PDE-inhibitor selectivity.
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.