The Helical virtual AI lab for pharma, an application layer that turns biological foundation models into decision-ready, reproducible in-silico discovery workflows. The $10 million funding will support expansion across more top-20 pharma programs and growth of its deployed science engineering team.
London, UK – Apr 15; Pharma has no shortage of ideas. It has a shortage of throughput. Roughly 50 new drugs are approved each year despite more than 10,000 known diseases, and every promising hypothesis still collides with the same constraint: slow, expensive physical experimentation. Biological foundation models have opened the door to a new mode of discovery, where scientists can test hypotheses computationally before committing to the wet lab. Helical was built to make that shift real inside modern pharma R&D.
Today, the company announced a $10 million seed round led by redalpine with participation from Gradient, BoxGroup, Frst and notable angels including Aidan Gomez (CEO Cohere), Clement Delangue (CEO HuggingFace) and Mario Goetze (pro soccer player).
The timing reflects a gap that has emerged as bio foundation models have taken off. Pharma teams are excited about the model layer, but many efforts stall because the work between a model output and a scientific decision is still fragmented. New architectures are emerging constantly, while bench scientists and ML engineers operate in silos. As a result, teams often recreate one-off notebooks and analyses that are difficult to reproduce or transfer across programs. What pharma has needed is an application layer that turns powerful models into systems scientists can run, trust, and defend.
Helical is the virtual AI lab for pharma, designed to turn bio foundation models into reproducible discovery systems so every scientist can test hypotheses in-silico at the speed of inference. The platform has two product surfaces — the Virtual Lab for biologists and translational scientists, and the Model Factory for ML engineers and data scientists — built on the same data, the same models, and the same results. By putting both sides in the same system, Helical closes the gap between computational predictions and biological decision-making, so teams that traditionally worked in silos can collaborate on the same evidence.
“The models alone don’t discover drugs. The system does” said Rick Schneider, co-founder of Helical. “Pharma teams need a system that turns foundation models into workflows scientists can run, validate, and defend. We built Helical to make in-silico science reproducible at pharma scale, so teams can go from hypothesis to decision in days instead of months.”
Helical was founded in early 2024. The company was created by three school friends who took different paths into the same problem. Rick Schneider built tech at Amazon and later helped the German enterprise Celonis scale in France and Japan. Maxime Allard led data science teams at IBM before pursuing a PhD focused on reinforcement learning and robotics. Mathieu Klop became a cardiologist and genomics researcher. When bio foundation models emerged, the trio saw the chance to build the missing application layer that would let pharma teams move from model experimentation to reproducible, production discovery.
Helical is already in production with multiple top-20 global pharma companies, including a public collaboration with Pfizer on predictive blood-based safety biomarkers. Across deployments in target identification, biomarker discovery, and therapeutic design, teams have compressed discovery timelines from years to weeks and expanded organically from single indications into adjacent therapeutic areas.
The broader industry context is increasingly unforgiving. R&D spending exceeds $300 billion annually, timelines stretch beyond a decade, costs to bring a drug to market now exceed $2 billion on average, and more than 90 percent of candidates entering clinical trials fail. AI has been positioned as the answer, but many efforts stall in pilot because predictions alone are not enough. Discovery teams need outputs grounded in biological evidence, delivered through a system that makes decisions reproducible and explainable, not another black-box ranking.
“We are at a unique point in time where biological foundation models and general language reasoning models are converging.” Said 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.”
Looking ahead, Helical plans to deepen deployments across more therapeutic areas and programs with existing clients, expand to additional top-20 pharma organizations, and continue building the compounding evidence layer that improves performance across diseases. The company’s mission is to make every scientist able to test hypotheses at the speed of inference and to turn in-silico discovery into a reliable engine for R&D throughput.
