Helical raises $10M to scale its virtual AI lab, turning bio foundation models into reproducible in-silico workflows for faster, more reliable drug discovery in pharma.
Luxembourg-based startup Helical has raised $10 million to scale its “virtual AI lab,” a platform designed to turn biological foundation models into reproducible, decision-ready workflows for pharmaceutical R&D.
The round was led by redalpine, with participation from Gradient, BoxGroup, Frst, and and notable angels including Aidan Gomez (CEO Cohere), Clement Delangue (CEO HuggingFace) and Mario Goetze (pro soccer player). It follows Helical’s earlier €2.2 million seed round and highlights rising investor interest in AI infrastructure for biotech.
“We are at a unique point in time where biological foundation models and general language reasoning models are converging,” says Daniel Graf, General Partner at redalpine. “We backed Helical because we strongly believe they have what it takes to build the pharma AI orchestration platform that will drive this transition from siloed AI models to integrated virtual AI labs.”
“Scientists need to iterate around hypotheses in a structured way, like in a wet lab—but in silico.”
Maxime Allard, co-founder and CTO of Helical
Founded in 2023, Helical started as an open-source platform giving researchers access to bio foundation models trained on DNA and RNA data. But the team quickly identified a key limitation: access to models alone does not translate into usable scientific decisions.
“From the beginning, we knew a model platform alone would not be enough to help pharma companies get insights,” explains co-founder and CTO Maxime Allard. “Scientists need to iterate around hypotheses in a structured way, like in a wet lab—but in silico.”
This led to Helical’s evolution into a “virtual AI lab,” an application layer that unifies workflows across biologists, bioinformaticians, and ML engineers. The platform includes two components: a Virtual Lab for scientists and a Model Factory for technical teams, both sharing the same data, models, and outputs.
Allard describes the shift as closer alignment with real scientific practice. “Scientists don’t test a hypothesis once—they iterate, refine, and explore diseases from multiple angles. The Virtual Lab enables that iterative process computationally.”
The platform addresses a major inefficiency in pharma R&D: fragmented tools and non-reproducible analyses. Despite massive investment, discovery processes still rely on siloed notebooks and one-off experiments that are difficult to reproduce or scale.
“The models alone don’t discover drugs. The system does.”
Rick Schneider, co-founder of Helical
Helical aims to standardise in-silico experimentation, allowing hypotheses to be tested computationally before wet-lab validation. Early deployments suggest timelines can shrink from years to weeks.
The company already works with several top-20 pharmaceutical firms, including Pfizer on predictive safety biomarkers, and Tanabe Pharma. These collaborations reinforce Helical’s thesis that the bottleneck in drug discovery is no longer model availability, but system integration.
Reproducibility is central to the platform. “Our Virtual Lab is built so every experiment can be reproduced consistently,” says Allard. “This is increasingly important as regulators like the FDA require transparency and reproducibility for AI in drug discovery.”
Trust is another core pillar. Instead of standalone predictions, Helical embeds validation criteria directly into workflows so scientists can assess and defend results before wet-lab execution.
“Validation must be part of the system,” Allard adds. “Only then can computational results reliably translate into biological validation.”
The company’s approach reflects a broader industry shift: value is moving from model development to orchestration layers that connect AI systems to real scientific workflows.
However, challenges remain. “Biology spans multiple modalities,” Allard notes. “To improve in-silico discovery, models must adapt across these modalities and to specific use cases like biomarker discovery or target identification.”
Looking ahead, Helical plans to expand across therapeutic areas and deepen deployments with major pharma companies. Its ambition is to make in-silico discovery a standardised engine for drug development. If successful, Helical could help redefine pharmaceutical R&D—turning AI from a promising tool into core infrastructure for modern drug discovery.
