AI target discovery engine

We identify the best candidate targets in a given indication

See how it works

AI-powered therapeutic target discovery

Challenge

Targets don’t convert to drugs

Patient data is underutilized
Pre-clinical models lack translatability to human biology
Disease heterogeneity is not captured

Solution

Owkin’s TargetMATCH

TargetMATCH is 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

Using AI for target discovery

How does TargetMATCH work?

Best-in-class data

Multimodal patient data: Private datasets

Large cancer patient cohorts from partner hospitals with:

Deep clinical data
Latest SoC response
8 million data points per patient
6 data modalities

Multimodal patient data: Public datasets

Tens of thousands of patients from:

Pan-cancer public datasets (TCGA)
Disease-specific datasets (MESOMICS)

Pharma data from partners

We can enrich the discovery dataset by integrating proprietary datasets.

Biomedical literature

We use publicly available datasets and biomedical resources to characterize drug targets.

OpenTargets, GTEX, CT.gov, ChEMBL, and more.

AI characterization

Integration of multimodal data

We use AI to Integrate multiple data modalities in a unified environment.

Patient characterization

We use representation learning to extract features from millions of data points from different data modalities:

H&E, WES, Clinical data, RNA-seq, single-cell RNA-seq, Spatial transcriptomics
4,000 AI features per patient

Target characterization

Targets are represented by vectors of features extracted from public biomedical resources:

50 AI features per target

AI reasoning

Target/patient pairing

Our AI builds target-patient pairs by computing the importance of a target in a subpopulation along many axes e.g:

Patient survival
Essentiality
Features of the tumor microenvironment (TME)

Target/patient subgroup pair ranking

We find the best patient subgroup for every target using an innovative AI optimization method that answers the question:

Which patient subgroup would best respond to targeting gene X?

Biomedical review

In-house team of MDs and PhDs in biology and pharmacology
Top external KOLs and clinicians in the field

Discovery

Top 5 targets and associated patient subgroups
Actionable biomarkers for clinical success

AI target discovery

Why work with Owkin?

Access to patient data and samples

Access to up-to-date patient data representing the latest standard-of-care. Use cutting-edge new modalities such as spatial omics in drug discovery.

Multimodal AI

Experts in integrating multiple modalities to capture the tumor microenvironment for a multiscale understanding of disease biology and for the identification of multimodal AI targets.

Interpretable AI

Allows medical experts to critically examine the model rationale and generate new hypotheses to continuously improve models.

Large Language Models

We integrate Large Language Models (LLMs) in drug discovery to enhance our team’s expertise and scale target discovery.

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Owkin has delivered 3 new AI-discovered candidate drug targets to Sanofi

Proven track record

Owkin has delivered 3 new AI drug targets to Sanofi

"I'm excited about the Sanofi and Owkin collaboration and its potential to transform drug discovery. Owkin's data network and AI capabilities, combined with Sanofi's expertise, can potentially lead to new treatments and better patient outcomes."

Frank Nestle, MD

Global Head of Research and Chief Scientific Officer, Sanofi

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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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AI drug positioning

Matching the right drug and patient for better responses

For a given drug, we identify novel disease indications and subgroups for development, by aggregating causal evidence from prior knowledge and multimodal patient data.

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