DrugMATCH

AI drug positioning engine

Applying AI to match a drug asset with new indications or subpopulation of existing indications
See how it works
Challenge

90% of blockbuster medicines only work for 30-50% of patients

  • Lack understanding of TME and drug mechanism of action
  • Incomplete knowledge of clinical heterogeneity
  • Testing all possible drug combinations is impractical
Solution

Owkin’s DrugMATCH

  • DrugMATCH is a suite of end-to-end tools that uses knowledge graphs, graph machine learning, biomedical literature and multimodal patient data to identify novel disease indications and associated subgroups for a given drug

Drug positioning engine

How does DrugMATCH work?

DrugMATCH can be used for AI drug repurposing, also know as drug positioning, or indication or label expansion, which involves using AI to find new therapeutic uses for existing drugs. We apply DrugMATCH to drug positioning in immunology, oncology and other therapy areas.

Best-in-class data

Broad data access
We access patient data for hundreds of disease indications from public datasets.
We integrate this data with biomedical literature and multimodal patient data from our network of partners
Best-in-class data

Knowledge graph

Drug asset in broad disease space
Our knowledge graph represents known interactions between drugs, targets and indications.
  • Tens of thousands of nodes
  • Millions of connections between nodes
Drug asset in broad disease space

AI drug-disease matching

Prioritization of best drug-disease pairs
Our graph ML methods learn the patterns that made drugs/targets effective during past clinical trials.
We use AI to predict the best disease indications to potentially target with the drug of interest.
AI drug positioning

AI subgroup matching

Positioning of drug on patient subpopulations
Deep dive into a given drug-disease pair, to characterize the patient subtypes most likely to respond to that drug.
  • Owkin data access
  • Multimodal patient embeddings
  • ML subpopulation optimization
AI subgroup optimization

Discovery

  • Top indications potentially affected by the drug
  • Matched patient subpopulations to increase treatment efficacy
Delivery

AI drug positioning

Why work with Owkin?

graph machine-learning expertise
Carefully constructed knowledge graphs

Expert team of biologists, medical experts and data scientists combine multiple data modalities to build quality knowledge graphs and disease maps.

World-class graph machine-learning expertise
World-class graph machine-learning expertise

We use graph neural networks & advanced interpretable AI to identify key regulators and high/low responder subgroups.

Carefully constructed knowledge graphs
Unmatched access to patient data

Our AI precision medicine solutions are powered by patient data to capture disease heterogeneity and the TME for a multiscale understanding of disease biology.

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Subgroup discovery

Multimodal AI-powered biomarkers

We combine cutting-edge machine learning and biology to identify biomarkers.

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TargetMATCH

A new approach to target discovery

A suite of end-to-end tools that uses multimodal patient data as the input, and outputs the top candidate targets and paired patient subgroups that would most benefit from therapeutic intervention on these targets.

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