Learn to use DrugBank, a free, web-based tool that combines chemoinformatics with bioinformatics. Explore both the chemical and biological nature of drugs in silico using DrugCards, the functional units of DrugBank. Each DrugCard represents a unique drug in the database and contains over 100 information fields collated from numerous scientific sources. DrugCards provide extensive information on approved drugs, biotech drugs, small molecules, experimental drugs, and nutraceuticals, yet DrugCards are easy to read and shuffle through. You can query DrugBank with either chemical or biological terms, including the protein targets of drugs or the enzymes that metabolize them. A new feature correlates genetic single nucleotide polymorphisms with adverse drug reactions and drug effectiveness. Ideal for the student, scientist, drug discoverer, clinician, pharmacist, or general public, DrugBank will help you find the answers to your questions about pharmaceuticals and their underlying biological effects.
You will learn:
This tutorial is a part of the tutorial group Human variations. You might find the other tutorials in the group interesting:
GAD: Genetic Association Database: An archived database associating human genes and polymorphisms with diseases
Madeline 2.0: Human pedigree diagram tools
DGV: Database of Genomic Variants: Database of Genomic Variants, DGV, catalogs and displays structural variation in the human genome
OMIM: Online Mendelian Inheritance in Man (OMIM): A database of human genes, genetic diseases and disorders
CGAP: Characterize the molecular genetic changes that cause a normal cell to become a cancer cell
ENCODE Foundations: ENCyclopedia of DNA Elements
GeneSNPs: An integrated view of gene structure and SNP variations
NIEHS SNPs: National Institute for Environmental Health Sciences Environmental Genome Project (EGP) SNPs
HapMap: HapMap, a database and analysis resource of human variation
Genetics Home Reference: A collection of data describing the effects of genetic variability on human health and disease
dbGaP: A database of genotypes and phenotypes with extensive variation data and clinical details
SeattleSNPs: Human SNPs in genes
dbSNP: NCBI's SNP database
GeneTests: GeneTests, a current, comprehensive genetic testing resource
Genome Databases (euk) : Genomic databases or repositories primarily aimed at eukaryotic organisms. Some may contain prokaryotic data as well.
Friday SNPpets: This week's SNPpets consist of an abbreviated week, because I'm on the road for a conference. But there's plenty of interesting stuff already this week, and #BoG16 is just getting going. As usual, new ...
Friday SNPpets: Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don't make it to a blog post. Here they are for your e...
Video Tip of the Week: VnD Resource for Genetic Variation and Drug Information: In today's tip I am going to feature a resource that I found recently. I've been updating our dbSNP tutorial, which Mary & Trey will be presenting at workshops in Morocco, and also our free PDB tutori...
Recent BioMed Central research articles citing this resource
Zhang Wen et al., Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data Networks analysis. BMC Bioinformatics (2017) doi:10.1186/s12859-016-1415-9
Sharp E Mark et al., Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources. Journal of Biomedical Semantics (2017) doi:10.1186/s13326-016-0110-0
Cihoric Nikola et al., Current status and perspectives of interventional clinical trials for glioblastoma – analysis of ClinicalTrials.gov. Radiation Oncology (2017) doi:10.1186/s13014-016-0740-5
Bertoni Natália et al., Integrative meta-analysis identifies microRNA-regulated networks in infantile hemangioma Clinical-Molecular Genetics and Cytogenetics. BMC Medical Genetics (2016) doi:10.1186/s12881-015-0262-2
Huang Chien-Hung et al., Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory. BMC Bioinformatics (2016) doi:10.1186/s12859-015-0845-0