Owkin

Company URL: https://www.owkin.com/

Year of Establishment: 2016

Founders:

• Dr. Thomas Clozel: A clinical research doctor and former assistant professor in clinical hematology. He is the son of Jean-Paul and Martine Clozel, founders of Swiss biotech company Actelion.

• Dr. Gilles Wainrib: A professor of artificial intelligence in biology and a pioneer in the field of machine learning applications in healthcare.

Company Description: Owkin operates at the intersection of artificial intelligence and biotechnology, focusing on transforming drug discovery and development processes. By integrating multimodal patient data—including genomics, imaging, and clinical information—with advanced AI models, Owkin uncovers complex biomarker patterns and disease mechanisms. This approach enables the identification of precision therapeutics, optimization of clinical trials, and development of AI-powered diagnostic tools. The company's mission is to harness the synergy between human expertise and artificial intelligence to deliver better drugs and diagnostics at scale, ultimately improving patient outcomes across oncology, cardiovascular, and immunological diseases.

Funding Status: As of 2025, Owkin has raised over $300 million in funding. Notable investments include:

• November 2021: $180 million equity investment from Sanofi, elevating Owkin's valuation to unicorn status (over $1 billion).

• June 2022: $80 million investment from Bristol-Myers Squibb.

Other investors encompass venture funds such as F-Prime, GV (formerly Google Ventures), and Bpifrance.

AI Technologies Used: Owkin employs several advanced AI technologies, including:

• Federated Learning: A decentralized machine learning approach that trains algorithms across multiple decentralized devices or servers holding local data samples without exchanging the data itself. This technique enhances data privacy and security.

• Transfer Learning: Utilizing knowledge gained from one problem to solve related problems, particularly effective when data availability is limited.

• Multiscale AI Models: Connecting data across cellular, molecular, and tissue scales to capture the causal links of complex biology.

These technologies are integrated into Owkin's platform to analyze multimodal data, discover novel biomarkers, and develop predictive models for drug discovery and diagnostics.

Use Cases:

1. MSIntuit® CRC: An AI-powered diagnostic tool that prescreens for microsatellite instability in colorectal cancer using digitized histology slides, optimizing MSI testing.

2. RlapsRisk® BC: A diagnostic solution that assesses the risk of relapse in early breast cancer patients, aiding in personalized treatment planning.

3. MOSAIC Project: The Multi Omic Spatial Atlas in Cancer is an initiative led by Owkin to create a comprehensive spatial multi-omics atlas for cancer research, enhancing the understanding of tumor structures and guiding new treatments.

Team Size: As of 2024, Owkin employs approximately 470 individuals across its offices in Paris, Nantes, New York, Boston, London, Geneva, and Berlin.

Traction: Owkin has established strategic partnerships with leading pharmaceutical companies, including Sanofi, Bristol-Myers Squibb, and MSD, to enhance their drug discovery and development processes using AI. The company has also collaborated with top academic centers to curate and analyze multimodal patient data, contributing to significant scientific publications and advancements in precision medicine.

Competitors:

• Atomwise: Utilizes AI for small molecule drug discovery, focusing on deep learning technologies to predict bioactivity and identify potential drug candidates.

• Genesis Therapeutics: Combines AI with biophysics to discover and develop novel therapies, particularly targeting previously undruggable proteins.

• Insitro: Integrates machine learning and biology to accelerate drug discovery and development, employing high-throughput data generation and predictive models.

• PathAI: Specializes in AI-powered pathology, providing tools for accurate and efficient diagnosis of diseases through digital pathology solutions.

• X-37: Focuses on AI-driven drug discovery, aiming to optimize the development of therapeutics by predicting clinical trial outcomes and drug efficacy.

Potential Challenges or Issues:

1. Data Privacy and Security: Ensuring patient data confidentiality, especially when collaborating across international borders with varying regulations.

2. Regulatory Approvals: Navigating the complex and evolving regulatory landscape for AI-driven diagnostics and therapeutics to achieve timely approvals.

3. Integration into Clinical Workflows: Seamlessly incorporating AI solutions into existing medical practices and ensuring user adoption among healthcare professionals.