Our approach
AI-powered
drug discovery
We combine cutting-edge machine learning and biology to advance drug discovery
What we do
We develop state-of-the-art, AI-based computational technologies from rich, multimodal patient data to discover new treatments
Context
Drug discovery is becoming slower and more expensive over time
Challenges
We need better drugs, better targets, and better clinical trials
Challenge 1
Targets don’t convert to drugs
- Patient data is underutilized
- Pre-clinical models lack translatability to human biology
- Disease heterogeneity is not captured
Challenge 2
Drug responses vary between patients
- Lack understanding of tumor microenvironment
- Lack understanding of drug mechanism of action
- Incomplete knowledge of clinical heterogeneity
Owkin's solution
AI engines to revolutionize drug discovery
We have pioneered two new drug discovery engines to deliver new drug targets and optimize drug positioning. Powered by AI, applied to multimodal patient data and prior knowledge, they are designed to capture causal evidence and gain a multiscale understanding of complex patient biology and treatment outcome heterogeneity.
Owkin combines cutting-edge machine learning and biology to advance drug discovery.
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"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
Subgroup discovery
Multimodal AI-powered biomarkers
We combine cutting-edge machine learning and biology to identify biomarkers.
Target discovery
A new approach to target discovery
We identify 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.
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.