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Overview
Owkin uses artificial intelligence to improve medical diagnostics and speed up the development of new drugs, specifically within the field of oncology. The company builds machine learning models that analyze diverse patient data, such as tissue samples and genetic records, to help researchers predict how patients will respond to specific treatments during clinical trials. Unlike many competitors, Owkin utilizes a collaborative network of academic centers and pharmaceutical companies to train its AI on vast, diverse datasets while maintaining strict data privacy. Their goal is to advance precision medicine by making the drug discovery process faster, less expensive, and more accurate for patients.
About Owkin
Industries
Data & Analytics
AI & Machine Learning
Biotechnology
Healthcare
Company Size
201-500
Company Stage
Late Stage VC
Total Funding
$368M
Headquarters
Paris, France
Founded
2016
Your Connections
People at Owkin who can refer or advise you
Simplify's Take
What believers are saying
- Sanofi’s five-year K Pro licence expands platform revenue across its full R&D chain.
- AstraZeneca’s three-year K Pro deal validates near-term demand for agentic pharma workflows.
- BeOne’s INVOKE trial supply agreement links Owkin-discovered biology to active oncology validation.
What critics are saying
- Sanofi can internalize K Pro and pressure pricing after platform embedding.
- AstraZeneca and Sanofi concentration makes any delivery miss highly visible.
- Clinical or regulatory failure would undermine Owkin’s patient-data and biological-reasoning thesis.
What makes Owkin unique
- K Pro combines multimodal patient data with biological reasoning models for pharma decisions.
- Owkin’s federated learning network spans 800+ hospitals, preserving privacy while training models.
- CE-marked diagnostics, including MSIntuit CRC and RlapsRisk BC, strengthen clinical credibility.
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Funding
Total Funding
$368M
Above
Industry Average
Funded Over
9 Rounds
Growth & Insights and Company News
6 month growth
↑ 8%1 year growth
↑ 9%2 year growth
↑ 11%Why AI models need patient data to deliver in drug discovery. Despite rapid advances in AI, many drug discovery models still struggle to translate computational predictions into clinical outcomes. Thomas Clozel explains how Owkin is training AI on large-scale patient-derived data while integrating experimental and clinical validation directly into model development. Artificial intelligence is now used across multiple stages of pharmaceutical R&D, including target identification, biomarker discovery, molecule design and clinical trial analysis. However, despite advances in generative AI and predictive modelling, relatively few systems have demonstrated clear clinical impact. One of the major challenges is that many AI models are still trained primarily on laboratory datasets or controlled experimental systems, which may not fully reflect the biological complexity seen in patients. As a result, predictions that perform well in computational or preclinical settings do not always translate successfully into clinical outcomes. Researchers are now exploring whether training AI systems on large-scale patient-derived data, combined with experimental and clinical validation, could improve how these models identify disease mechanisms, stratify patients and support drug development decisions. Dr Thomas Clozel, Co-founder and CEO of Owkin, believes patient-derived data will be essential for improving the biological relevance of drug discovery models. Over the past decade, Owkin has built multimodal patient datasets collected from a network of more than 800 hospitals, which the company uses across research and clinical applications. Building on this work, Owkin earlier this year announced a new agentic AI Scientist trained on patient-derived data, alongside partnerships with NVIDIA and Anthropic focused on integrating pathology analysis tools into healthcare AI workflows. The limits of current AI models. Clozel believes many AI systems used in drug discovery perform well on curated datasets but struggle when applied to real-world clinical settings. "So far none of these AI models have allowed us to cure cancer - so bluntly, we've failed," said Clozel. So far none of these AI models have allowed us to cure cancer - so bluntly, we've failed. He explained that many existing systems still lack sufficient understanding of the underlying biology of disease, limiting their ability to generalise across patient heterogeneity, evolving clinical practice and multimodal clinical data. This can create situations where AI systems generate promising predictions or novel molecules without adequately accounting for target validity, translational relevance or patient stratification. AI needs a reality check, or it risks spinning out more and more predictions that sound good, but fall apart on contact with the real world. "AI needs a reality check, or it risks spinning out more and more predictions that sound good, but fall apart on contact with the real world," he added. To address this, Owkin is integrating experimental and clinical validation directly into model development. Clozel said the company tests predictions using patient-derived cells and organoids, while findings from clinical studies, including Owkin's INVOKE oncology trial, are also fed back into model training workflows. Why patient-derived data matters. A major part of Owkin's strategy involves training models on real-world patient data rather than relying primarily on laboratory or brokered datasets. Traditional drug discovery workflows often begin with in vitro systems and experimental models designed to simulate aspects of human disease. However, these systems frequently fail to capture the biological complexity observed in patients. Real-world patient data can instead provide information on disease heterogeneity, co-morbidities, prior treatments and biological variation across multiple scales, including tissue morphology, molecular profiles and clinical outcomes. According to Clozel, this level of biological fidelity may help researchers identify more meaningful biomarkers, define clinically relevant patient subgroups and improve clinical trial design. From predictive software to agentic systems. Owkin is also developing what it describes as an agentic AI Scientist designed to support multi-step scientific workflows rather than simply generate isolated predictions. Clozel described this transition as moving from "software that predicts" towards "systems that act". This AI Scientist can retrieve multimodal datasets, perform analyses, test assumptions and generate outputs designed for use within research and clinical development workflows. Researchers may also be able to analyse patient subgroups, interrogate raw datasets and generate visualisations without requiring multiple separate analytical steps or external bioinformatics support. As part of the AI Scientist, Owkin is also developing specialised interoperable AI tools, including pathology analysis systems capable of identifying cellular features directly from pathology slide images. Clozel said the goal is to reduce interruptions within research workflows and allow scientists to iteratively explore hypotheses while interacting directly with underlying biological data. Extracting biological information from pathology slides. One area of focus for Owkin involves extracting additional biological information from routine pathology slides. Clozel noted that standard haematoxylin and eosin (H&E) slides contain large amounts of underused biological information relating to tumour microenvironments, spatial organisation and cellular composition. However, large-scale pathology analysis has historically been limited by cost, time and analytical complexity. Clozel said Owkin's Pathology Explorer AI tool is designed to automate parts of this process, helping researchers analyse pathology images more efficiently. Testing biological reasoning in AI systems. Clozel said Owkin's long-term goal is the development of what the company describes as Biological Artificial Super Intelligence (BASI), referring to AI systems designed to reason across biological systems rather than simply generate predictions from datasets. For Clozel, one of the most important milestones will be demonstrating that AI-generated predictions can be validated experimentally and clinically. This includes testing model outputs using patient-derived organoids and evaluating whether AI-informed approaches improve clinical trial design or patient response prediction. We want to build an AI scientist that can be proactive in its exploration and development of hypotheses. Owkin is also working towards what Clozel described as an "autonomous AI scientist", capable of independently generating hypotheses, testing predictions and iteratively refining its understanding of biological systems. "We want to build an AI scientist that can be proactive in its exploration and development of hypotheses," said Clozel. Ultimately, Clozel believes the strongest evidence for these systems will come when they can generate novel biological hypotheses that prove experimentally and clinically valid. "The greatest milestone, though, will be when we have created a system that can bring you a biological hypothesis... that is surprising, new and turns out to be true," he concluded. Thomas Clozel M.D. Dr Thomas Clozel leads Owkin as CEO. He is a former Assistant Professor of Clinical Onco-haematology at Hopital Henri Mondor in Paris and former member of Ari Melnick's lab at the Weill Cornell Medical College. Clozel ensures that patient health is prioritised when developing breakthrough medical technologies, bringing a patient-centric approach to every Owkin project. View full profile
Waiv, a Paris-based startup developing AI-powered diagnostic tests in oncology, has raised $33 million in a Series C round led by OTB Ventures and Alpha Intelligence Capital. The funding, comprising approximately 85% equity and the remainder in non-dilutive financing, also includes participation from Serena Data Ventures, Karista and Sista Fund. Led by CEO Meriem Sefta, former Chief Diagnostic Officer at Owkin, Waiv analyses microscopic tumour images using AI algorithms to better characterise cancers and guide treatment decisions. The company emerged as a spin-out from Owkin, which remains a minority shareholder but has no operational governance role. Waiv will use the funding to expand adoption of its biomarker identification tests and strengthen pharmaceutical partnerships with companies including Merck and AstraZeneca.
Owkin announces partnership with Consensus to strengthen literature intelligence for Owkin's AI Scientist, K Pro. * Users of K Pro, Owkin's AI Scientist for biology, will be able to access broad research coverage and generate answers grounded in 200M+ peer-reviewed scientific articles Paris, France - March 3rd, 2026 - Owkin, an AI company on a mission to build biological artificial superintelligence to cure all disease, today announced a partnership with Consensus, the leading AI-powered research search engine, to bring enhanced scientific literature intelligence into Owkin's AI Scientist for Biology, K Pro. Through this partnership, K Pro users will be able to generate answers grounded in full-text scientific articles - excluding redacted articles - with additional controls to refine results based on recency, citations, and journal reputation and impact. Consensus mitigates the risk of hallucination, guaranteeing every paper cited is real and every summary based on research. The integration is designed to support more precise scientific research workflows, complementing Owkin's expertise in multimodal analysis and data-driven biology. Pascal Weinberger, Owkin's Co-CEO said: "By combining Owkin's agentic data driven approach to biological research with Consensus's literature intelligence, we aim to help teams move faster from scientific question to evidence-based insight." Eric Olson, CEO of Consensus said: "Consensus is focused on making research more trustworthy and usable at the point of decision. We're excited to partner with Owkin to bring high-confidence literature intelligence into workflows that matter for biology and medicine." About Consensus. Consensus is a San Francisco-based startup building the OS for research. Its AI-powered research engine helps millions of students, researchers, and doctors analyze scientific papers and complete literature reviews 10x faster, applying the latest in AI agents to make the world's best researchers dramatically more productive. Consensus partners with over 170 universities and many of the world's leading biotech companies to accelerate science globally, and works with major academic publishers - including Sage, ACS, Taylor & Francis, and more - to bring full-text insights directly to scientists. Backed by USV and top AI investors, and featured in The Wall Street Journal, The New York Times, and Nature, Consensus is on a mission to empower the world to understand, create, and apply good science. About Owkin. Owkin is an AI company on a mission to solve the complexity of biology. It is building the first Biology Super Intelligence (BASI) by combining powerful biological large language models, multimodal patient data, and agentic software. At the heart of this system is Owkin K, an AI copilot and its new LLM finetuned on biology called Owkin Zero, used by researchers, clinicians, and drug developers to better understand biology, validate scientific hypotheses, and deliver better diagnostics and therapies faster.
NHS Lothian joins Owkin's patient data network with multi-year partnership. Owkin, a leading agentic AI company, and NHS Lothian, Scotland's second largest health board, have entered a multi-year partnership - providing the foundations to build agentic AI that can accelerate breakthroughs and advance drug discovery. As part of the collaboration, Owkin will work with NHS Lothian NRS Bioresource, a REC-approved infrastructure established in 2010 to support human tissue research by facilitating access to de-identified tissue samples, digital pathology whole slide images and associated data under the appropriate approvals and governance. Under the agreement, Owkin will access digital pathology images and associated metadata from the NRS Bioresource to apply AI techniques to identify new drug candidates, de-risk and accelerate clinical trials, and develop diagnostic solutions aimed at improving patient outcomes. The partnership builds on NHS Lothian's membership of ATLANTIS - an extensive multimodal patient data discovery program across 7 countries in 11 therapeutic areas. This program has provided the resource for NHS Lothian to discover specific tissue and associated datasets across their vast oncology patient pool, making them an available resource for future research. More recently, NHS Lothian has partnered on Owkin's project to develop an AI-powered product to screen for gBRCA mutations (BRCAura(R)) in breast cancer directly from digitised images of diagnostic biopsies without the need for other additional laboratory tests. NHS Lothian contributed digital images and data, as well as providing valuable feedback from experienced pathologists and oncologists on how such a diagnostic product could fit into current workflows. This is part of Owkin's broader patient validation strategy, ensuring Owkin's AI models are tested in real patient populations. By contributing to Owkin's multimodal data discovery effort, NHS Lothian will help strengthen the foundations for Owkin's agentic artificial intelligence infrastructure, helping to speed up drug discovery, reducing the time from development to patient impact by connecting research to care. Raman Ghuman, EU Head of Partnerships at Owkin said: "We are delighted to welcome NHS Lothian to the Owkin Data Network. This partnership allows us to combine our AI expertise with their leading healthcare knowledge, expanding our Patient Data network to advance biomedical research." Dr David Dorward, Consultant Pathologist & Digital Pathology Lead at NHS Lothian said: "This partnership agreement with Owkin marks an exciting next step in NHS Lothian's ongoing contribution to the development of clinically relevant digital pathology and AI tools designed to enhance diagnosis and improve patient care. Ultimately, this approach will benefit not only patients within NHS Lothian, but also those further afield." About NHS Lothian. NHS Lothian is the regional health board for Edinburgh, East Lothian, Midlothian and West Lothian, delivering acute, primary, community and mental health services across the area. It works with local authorities, universities and third-sector partners to improve population health and reduce inequalities. About Owkin. Owkin is an AI company on a mission to solve the complexity of biology. It is building the first Biology Super Intelligence (BASI) by combining powerful biological large language models, multimodal patient data, and agentic software. At the heart of this system is Owkin K, an AI copilot and its new LLM finetuned on biology called Owkin Zero, used by researchers, clinicians, and drug developers to better understand biology, validate scientific hypotheses, and deliver better diagnostics and therapies faster.
4baseCare partners with France-based Owkin to advance inclusive cancer research. 4baseCare, a precision oncology company building population-relevant clinico-genomic intelligence, has collaborated with France-based Owkin, an agentic AI company focused on Biological Artificial Superintelligence (BASI), to advance inclusive cancer research. Through this collaboration, the two companies will work together to convert fragmented cancer care data into AI-ready intelligence. The goal is to bridge the data gap and long-standing underrepresentation of Asian and Middle Eastern populations in cancer care and drug development. By building more representative datasets, the partnership aims to support more inclusive research and create more equitable standards of cancer care worldwide. 4baseCare's TarGT(TM) Indiegene test provides deeper insight into a patient's disease, enabling targeted treatment decisions instead of a one-size-fits-all approach. Owkin is an Agentic AI company building Biological Artificial Intelligence to transform drug discovery and development. It created the first Agent for Biology, K Pro, designed to connect research to care. Founded on the belief that precision health depends on AI powered by high-quality patient data, Owkin unlocks insights from fragmented biomedical datasets across hospitals and research centers. K Pro analyzes and structures this multimodal data into AI-ready assets, generating actionable insights that guide smarter biopharma decisions. By continuously integrating real-world patient data, K Pro drives biological discovery, de-risks and accelerates clinical trials, and advances diagnostics, directly helping bring better treatments to patients faster. Thomas Clozel, CEO and Co-Founder, Owkin, said, "We are on a mission to build biological artificial superintelligence to cure all diseases. Global patient data is the key to unlocking this. We are excited to enhance our collaborations in India to turn fragmented datasets into meaningful discoveries for more inclusive therapeutic breakthroughs." Kshitij Rishi, COO & Co-Founder, 4baseCare, said, "Precision oncology is only as strong as the data behind it. At 4baseCare, we build inclusive genomic datasets reflecting the diversity of the global south, non-Caucasian patients and combine them with advanced AI to enable more informed treatment decisions. We look forward to working with Owkin to ensure underrepresented populations are meaningfully included in global research and next-generation drug development."
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About Owkin
Industries
Data & Analytics
AI & Machine Learning
Biotechnology
Healthcare
Company Size
201-500
Company Stage
Late Stage VC
Total Funding
$368M
Headquarters
Paris, France
Founded
2016
There are no jobs for Owkin right now.
Find jobs on Simplify and start your career today