What happened
Rivercell, a Paris-based biotech startup, has raised $25 million (approximately €22 million) in seed funding. The round was led by German investor HV Capital, with participation from HCVC, Alven, and Bpifrance Digital Venture. The capital will be used to expand the company's automated wet lab in Paris, support its data generation platform, and develop its AI Virtual Cell program. The company aims to build AI models that predict how human cells respond to various interventions, such as drug treatments and genetic modifications, by generating purpose-built experimental data at scale.
Paris-based Rivercell has closed a $25 million seed funding round, led by HV Capital. The round also included participation from HCVC, Alven, and Bpifrance Digital Venture. According to European Biotechnology Magazine, the funds will support the expansion of Rivercell's automated wet lab in Paris and the development of its AI Virtual Cell program.
The company's core strategy involves building both the infrastructure to produce training data and the AI models that learn from it. Rivercell's platform measures individual cells after experimental interventions, such as drug treatment or genetic changes. It combines different types of biological measurements and tracks responses over time, rather than relying on static snapshots, to capture how cells change under treatment.
Co-founder and CEO Yann Fleureau stated that the missing piece in current drug discovery is data: specifically, how cells change over time and under treatment, seen through multiple lenses and at multiple layers, captured at scale. The company was founded in summer 2025 by Fleureau, who previously co-founded Cardiologs, an AI cardiac diagnostics company acquired by Philips in 2021. Eric Durand joined in 2026 as co-founder and chief scientific officer, bringing experience from Novartis, Owkin, and Bioptimus.
Source details: european-biotechnology.com ↗
Why it matters
This funding supports a shift in drug discovery from hypothesis-driven experimentation to data-driven prediction. By combining automated biological measurement with AI modeling, Rivercell aims to create a 'virtual cell' that can simulate cellular responses across multiple therapeutic areas, including oncology and immunology. If successful, this approach could significantly reduce the time and cost associated with preclinical drug testing by identifying promising candidates earlier and reducing the number of physical experiments required. The investment signals growing confidence in AI-driven biological simulation as a core infrastructure for pharmaceutical R&D.
Rivercell's approach addresses a critical bottleneck in pharmaceutical research: the high cost and slow pace of experimental validation. By using AI to predict cellular responses, the company aims to reduce the number of physical experiments needed to identify viable drug candidates. This could accelerate the discovery process for diseases including oncology, immunology, rare diseases, and cardiometabolic conditions.
The investment highlights a broader trend in biotech where AI is not just an analytical tool but a central component of the experimental workflow. By integrating automated wet labs with AI modeling, Rivercell is attempting to create a closed-loop system where data generation and model training are tightly coupled. This infrastructure-heavy approach requires significant capital, as evidenced by the $25 million seed round.
The potential impact extends beyond single therapeutic areas. If the AI Virtual Cell can learn the underlying rules of cellular responses, it could support discovery across multiple disease categories, offering a versatile platform for drug developers. This cross-disease applicability is a key differentiator for the company in the competitive AI biotech landscape.
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What to watch next
Key metrics to monitor include the size and quality of the generated by Rivercell's automated lab, the predictive accuracy of its AI models against independent benchmarks, and the timeline for launching the virtual cell platform. Investors and the scientific community will be watching to see if the company can demonstrate that its generated data reliably guides drug discovery decisions in real-world applications.
Rivercell has not disclosed the size of its current , specific prediction benchmarks, or a launch date for its virtual cell model. The next critical milestone is demonstrating that its experimental platform can produce useful training data at scale. Investors will be looking for evidence that the data generated is high-quality and sufficient to train robust AI models.
The reliability of the AI predictions in guiding actual drug discovery decisions will be a major focus. The company needs to show that its virtual cell simulations correlate with real-world biological outcomes. Independent validation of its models against existing datasets or clinical trial results will be essential to establish credibility in the scientific community.
Expansion of the automated wet lab in Paris will be a tangible indicator of progress. The scale of this infrastructure will determine the company's capacity to generate the diverse and large-scale datasets required for its AI models. Monitoring the pace of this expansion and any partnerships with pharmaceutical companies for data or validation will provide insights into the company's commercial trajectory.