Helical raises USD $10 million to expand virtual AI lab

Helical has raised USD $10 million in seed funding in a round led by Redalpine.

The London biotech startup will use the money to expand its virtual AI lab across more pharmaceutical programmes and grow its science and engineering team.

Gradient, BoxGroup and Frst also participated, alongside individual investors including Cohere Chief Executive Officer Aidan Gomez, Hugging Face Chief Executive Officer Clement Delangue and footballer Mario Goetze.

Helical pitches its software as an application layer that helps drug researchers use biological foundation models in day-to-day research workflows. The system is designed to let biologists, translational scientists, machine learning engineers and data scientists work from the same data, models and results.

The company was founded in early 2024 by Rick Schneider, Maxime Allard and Mathieu Klop. Schneider previously worked at Amazon and later at Celonis. Allard led data science teams at IBM before starting a PhD in reinforcement learning and robotics. Klop trained as a cardiologist and genomics researcher.

Pharma focus

Helical is already being used by several top-20 global drugmakers, including through a public collaboration with Pfizer on predictive blood-based safety biomarkers.

Its deployments span target identification, biomarker discovery and therapeutic design. Across those projects, teams have shortened discovery timelines from years to weeks and expanded work from single indications into nearby therapeutic areas.

The pitch comes as drugmakers look for ways to improve the productivity of research spending. Industry figures cited by Helical put annual R&D expenditure at more than USD $300 billion, while the average cost of bringing a drug to market exceeds USD $2 billion and more than 90 per cent of candidates entering clinical trials fail.

Interest in AI tools for drug discovery has grown rapidly, but many projects have remained at the pilot stage. One challenge for pharmaceutical companies has been connecting model outputs to scientific decisions that can be repeated, checked and applied across programmes, rather than confined to isolated experiments or notebooks.

Helical argues that this gap between model development and practical use inside research teams has held back broader adoption. Scientists and machine learning teams often work separately, making analyses harder to reproduce and transfer between projects.

“The models alone don’t discover drugs. The system does,” said Rick Schneider, Co-Founder, 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.”

Investor view

Redalpine said the investment reflects a wider shift in how AI is being used in life sciences, as advances in biological foundation models begin to converge with broader reasoning systems.

“We are at a unique point in time where biological foundation models and general language reasoning models are converging,” said Daniel Graf, General Partner, 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.”

Helical’s central claim is that drug discovery teams need more than model predictions. They need a system that records how those predictions were produced, links them to biological evidence and presents them in a form researchers can use to decide what to test next in the lab.

That argument points to a broader challenge in computational biology. Many AI-led discovery efforts have promised faster, cheaper research, but pharmaceutical companies still have to judge which findings are robust enough to support expensive laboratory and clinical work.

The technology is intended to reduce the time needed to move from an initial hypothesis to a decision on whether a programme should proceed. Helical says that process can be cut from months to days when in-silico workflows are set up in a repeatable way.

Like many AI startups in biotech, the company now faces the challenge of turning early deployments into long-term use across large research organisations. Its backers are betting that demand from major drugmakers for more consistent, auditable AI workflows will support that expansion.