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:

  • to understand and interpret DrugCard data
  • how to query and browse through DrugBank information
  • how to perform basic searches for specific drug information
  • to perform advanced queries via text, sequence, chemistry, structure, and other parameters
TUTORIAL RELATED CONTENT

TUTORIALS

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

CATEGORIES

Genome Databases (euk) : Genomic databases or repositories primarily aimed at eukaryotic organisms. Some may contain prokaryotic data as well.

BLOG POSTS

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...

BIOMED CENTRAL

Recent BioMed Central research articles citing this resource

Egieyeh Ayodele Samuel et al., Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs. Malaria Journal (2016) doi:10.1186/s12936-016-1087-y

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

Nascimento C. A. André et al., A multiple kernel learning algorithm for drug-target interaction prediction Networks analysis. BMC Bioinformatics (2016) doi:10.1186/s12859-016-0890-3

Yu Hasun et al., Prediction of drugs having opposite effects on disease genes in a directed network. BMC Systems Biology (2016) doi:10.1186/s12918-015-0243-2

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