Helical: the virtual AI lab powering reproducible drug discovery

the throughput problem in pharma

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 R&D spending now exceeds $300 billion annually with costs to bring a single drug to market exceeding $2 billion. Every promising hypothesis still collides with the same constraint: slow, expensive physical experimentation, and more than 90% of clinical candidates ultimately fail.

Biological foundation models have opened the door to a new mode of discovery, one where scientists can test hypotheses computationally before committing to the wet lab. But most pharma teams have stalled in the gap between model output and scientific decision. New architectures emerge constantly while bench scientists and ML engineers operate in silos, recreating one-off notebooks and analyses that are impossible to reproduce or transfer across programs. Helical was built to close that gap.

three school friends, one shared problem

Founded in early 2024, Helical brings together a founding team whose paths diverged and converged around the same challenge. Rick Schneider built technology at Amazon before helping scale the German enterprise Celonis across France and Japan. Maxime Allard led data science teams at IBM before pursuing a PhD in reinforcement learning and robotics. Mathieu Klop became a cardiologist and genomics researcher. When bio foundation models emerged, the three saw a clear opportunity to build the application layer pharma was missing. 

the virtual AI lab for pharma

Helical is the virtual AI lab for pharma: an application layer that turns biological foundation models into decision-ready, reproducible in-silico discovery workflows. 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. Teams that have traditionally worked in silos can now collaborate on the same evidence, with outputs that are grounded in biology, explainable, and defensible across programs. "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."

already in production at pharma scale

Helical is already deployed with multiple top-20 global pharma companies, including a public collaboration with Pfizer on predictive blood-based safety biomarkers. Across programs spanning 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.

backing the pharma AI orchestration layer

We are delighted to lead Helical's $10 million seed round, with participation from Gradient, BoxGroup, Frst, and notable angels including Aidan Gomez (CEO, Cohere), Clement Delangue (CEO, HuggingFace), and Mario Götze. "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."

what's next for Helical

With this funding, Helical will deepen deployments across more therapeutic areas and programs with existing clients, expand to additional top-20 pharma organisations, and continue building the compounding evidence layer that improves performance across diseases. Their 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.

We are proud to back Rick, Maxime, Mathieu, and the entire Helical team as they build the infrastructure for the next generation of medicine.