Discover more,
remarkably fast

Uncover insights sooner than ever with our machine-learning ready drug data.

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Drug discovery is difficult and expensive

You can't advance medicine if you're stuck wading through outdated, contradictory data.

The journey to clinical trial just got easier

For AI-powered drug discovery & repurposing, our datasets dramatically reduce the time & money it takes to get started, so you can successfully bring a drug or treatment to trial.

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Train your ML models
with ground
truth data

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Enhance your data pool
with structured
information

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Build predictive models
with a huge range of
data features

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Research with confidence

Our drug data is:

  • Structured & machine-learning ready
  • Inclusive of clinical & molecular information
  • Highly connected & links to numerous identifiers
  • Curated by medical & scientific experts
  • Updated daily

Giving you the power to discover more.

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Explore our datasets

The unrivalled range and detail of our machine-readable drug data can take you all the way from big idea to clinical trial.

Most requested drug discovery datasets:

Adverse Effects
Use known side effects of existing drugs to build and validate your predictive models.
Drug Categories
Categorize and organize metadata groups of drugs by chem structure, mechanism of action, and taxonomy.
Indications
Use on- and off-label indications to group drugs together for grouping techniques in ML.
Metabolism
Understand metabolic reactions and pathways to build out and enhance predictive models.
Protein Relationships
Information about bonding to enzyme and protein reactions helps with building and establishing your ML.
Targets
Use target information to build and validate your predictive models.

AI in Drug Discovery eBook

The future is here, and this time the robots are on our side. Drug discovery is faster, safer, and more exact, all because of AI.

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Chat with an expert about
our available datasets

For data science and machine learning, DrugBank datasets are the industry standard and trusted around the world.