The most valuable data in drug discovery is also the most locked down. Pharmaceutical giants sit on proprietary datasets that could train better AI models, but privacy laws, competitive walls, and patient consent make pooling that information impossible. Apheris, a Berlin-based startup, is building a market for that data without moving a single byte.
Founded in 2019, the company sells federated computing infrastructure. Its platform, the Apheris Gateway, allows separate organizations,like rival pharma companies or research hospitals,to collaboratively train machine learning models. The data stays behind each participant's firewall; only encrypted model updates are shared. The result is a network effect built on privacy, a counterintuitive but necessary wedge into the $1.5 trillion global pharma industry [apheris.com].
The Wedge: From Regulatory Pain to Product
Co-founder and CEO Robin Röhm experienced the problem firsthand. In a previous startup, he lost customers because data couldn't be centralized due to regulatory constraints [apheris.com]. That frustration became Apheris's founding thesis. The product is engineered for the specific realities of life sciences R&D: secure local use, benchmarking on in-house datasets, and controlled customization of shared models.
The clearest proof of its wedge is the ADMET Network, a federated initiative for predicting how compounds are absorbed, distributed, metabolized, excreted, and toxic in the body. Founding members include pharmaceutical firms Lundbeck, Orion Pharma, Recursion, and Servier [GEN Edge]. These are not startups, but established players with deep proprietary data. They are participating because Apheris's infrastructure lets them improve a shared model without ever seeing each other's raw information.
Building the Federated Stack
Technically, the offering is an end-to-end platform. The Apheris Gateway provides the orchestration layer, managing datasets, compute specifications, and training jobs across distributed environments. A command-line interface (CLI) allows data scientists to interact with the system, while a dedicated statistics package enables privacy-sensitive federated analytics [apheris.com].
The company has also aligned with major cloud and AI infrastructure players. It is an AWS Technology Partner and is listed as a provider of AI models for Amazon's Bio Discovery initiative [aboutamazon.com]. A technical blog post details work on advancing protein prediction using federated learning on NVIDIA's DGX Cloud [apheris.com]. These partnerships signal enterprise-grade integration, not just academic research.
The Team and the Tally Sheet
The founding duo brings a blend of domain and technical depth. Röhm studied medicine, philosophy, and mathematics before a stint in global banking at UBS. His co-founder and CTO, Michael Höh, holds a PhD in physics and computer science and previously built digital solutions and AI applications for industrial clients at Boston Consulting Group [apheris.com].
Investors have backed the team's approach with significant capital. Apheris closed a $20.8 million Series A round in January 2025. The round included lead investors OTB Ventures and eCAPITAL, with participation from Octopus Ventures, LocalGlobe, and Dig Ventures, among others [Crunchbase, Retrieved 2026] [TechCrunch, Jan 2025]. This brings the company's total disclosed funding to approximately $23.8 million.
| Metric | Value |
|---|---|
| 2020 Seed | 3.0 M USD (est.) |
| 2025 Series A | 20.8 M USD |
Where the Model Could Stumble
For all its technical promise, Apheris operates in a field with formidable hurdles and growing competition. The commercial model depends on convincing traditionally secretive enterprises to engage in collaboration, even a privacy-preserving one. The sales cycle is likely long and complex, targeting regulated R&D budgets.
On the technical side, federated learning introduces its own complexities. Model performance can suffer from data heterogeneity across sites, and the computational overhead of coordination is non-trivial. Furthermore, the competitive landscape is not quiet.
- Established Rivals. Companies like Owkin (with its Substra platform) and Rhino Health have a multi-year head start in the medical federated learning space.
- Platform Plays. Tech giants are moving in. NVIDIA offers its FLARE (Federated Learning Application Runtime Environment) as part of its Clara suite, providing a foundational toolkit.
- Specialized Startups. A cohort of AI-native life sciences companies, from Insilico Medicine to Savana, are building verticalized solutions that may bypass the need for a neutral infrastructure layer.
Apheris's answer rests on its focused life sciences positioning and its early network wins. Its partnership with Amazon Bio Discovery and the concrete ADMET Network launch provide a beachhead that pure-play infrastructure vendors lack [bio-itworld.com, 2026].
The Next Twelve Months
The $20.8 million Series A provides a multi-year runway to scale. The company is hiring for roles like an AI Network Strategist for Drug Discovery, indicating a push to land more flagship networks [Apheris Careers]. The key metric to watch will be the announcement of additional named pharmaceutical consortiums beyond ADMET.
Success will be measured in deployed networks and the aggregate value of the models they produce. Can Apheris move from enabling collaboration to becoming the indispensable transaction layer for private biomedical data? Investors like OTB Ventures and eCAPITAL have placed a $20.8 million bet that the answer is yes, betting that in the world of sensitive data, the only way to share is not to share at all. The question for the next round: how many more drug discovery pipelines will be running on federated infrastructure before a competitor's platform becomes the default?
Sources
- [apheris.com] Superior drug discovery models | https://www.apheris.com/
- [apheris.com] About Apheris | https://www.apheris.com/company
- [GEN Edge] ADMET Predictions Get AI Boost, Federated Data Network Unites Pharma
- [aboutamazon.com] Amazon Bio Discovery partners | https://www.aboutamazon.com/news
- [Crunchbase, Retrieved 2026] Apheris Funding Rounds
- [TechCrunch, Jan 2025] Apheris rethinks the AI data bottleneck in life science with federated computing | https://techcrunch.com/2025/01/02/apheris-rethings-the-ai-data-bottleneck-in-life-science-with-federated-computing/
- [bio-itworld.com, 2026] Article on Amazon Bio Discovery
- [Apheris Careers] AI Network Strategist - Drug Discovery role listing