AI target discovery

A new approach to target discovery

Owkin uses AI applied to multimodal patient data to find relevant targets and subgroups

Question

Why do current targets not convert to drugs?

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

Owkin's solution

AI Target Discovery Engine

Owkin’s AI target discovery engine identifies novel candidate targets with associated patient subgroups by applying interpretable AI models to multimodal patient data and aggregating causal evidence from prior knowledge using large language models.

Input: multimodal data

Clinical data
Histology
WGS/WES
Spatial
transcriptomics
RNA-Seq

Methodology

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Step 3
1
Unsupervised AI
to cluster patients based on multimodal patient data.
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Patient subgroups with
distinct biology.
Step 1
Data access
Gather, curate and prepare multimodal patient data
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Step 1
2
Interpretable AI
to analyze inter/intra differential clinical and biological characteristics to define subgroups.
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Characterised homogeneous subgroups.
Step 2
Patient characterization
Apply AI to multimodal patient data to characterize patients based on disease biology
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Step 3
3
Supervised AI
applied to regions of interest to predict which genes are associated with patient outcome, e.g. prognosis.
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Ranked list of molecular markers/biomarkers.
Step 3
Target characterization
Apply AI to prior knowledge to characterize targets based on genetic and molecular features
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Step 2
4
Target selector
to assess the expression, essentiality, mutation status and target safety of candidate targets.
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A ranked list of potential targets for the disease of interest.
Step 4
AI reasoning
Combine AI and human expertise to optimize and prioritize target-subgroup pairings

AI target discovery

Why work with Owkin?

Patient data
Access to patient data
and samples

Access to continuously enriched, up-to-date patient data representing the latest standard of care.

Multimodal AI
Multimodal AI

Experts in multimodal AI analysis to capture disease heterogeneity and the tumor microenvironment for a multiscale understanding of inter-patient response variability.

Interpretable AI
Interpretable AI

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

Work with us
"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
Global Head of Research and Chief Scientific Officer, Sanofi