Insider Brief
- Helical has raised a $10 million seed round led by Redalpine, with participation from Gradient, BoxGroup and Frst, to scale a platform that brings biological foundation models into production drug discovery workflows
- The company said its system combines a Virtual Lab for scientists and a Model Factory for ML engineers to enable in-silico hypothesis testing and bridge fragmented workflows between model outputs and scientific decision-making
- Helical is already working with multiple top-20 pharmaceutical companies, including Pfizer, using its platform across target identification, biomarker discovery and therapeutic design to reduce discovery timelines and improve R&D throughput
Helical has raised a $10 million seed round led by Redalpine, with participation from Gradient, BoxGroup and Frst, to scale a software platform designed to bring biological foundation models into production drug discovery.
Helical said it plans to use the funding to expand deployments across additional therapeutic areas, deepen work with existing pharmaceutical partners and further develop its platform as a system for integrating AI into end-to-end drug discovery workflows.
“The models alone don’t discover drugs. The system does,” co-founder Rick Schneider said in the announcement. “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.”
According to the London-based company, pharmaceutical companies face mounting pressure to improve R&D productivity, with roughly 50 new drugs approved each year despite more than 10,000 known diseases and development timelines that often exceed a decade. While advances in AI-driven biological models have accelerated early-stage research, the company pointed out that much of the work required to translate model outputs into scientific decisions remains fragmented across teams and tools.
Helical said its platform allows scientists to test hypotheses computationally before committing to physical experiments. It’s system combines a “Virtual Lab” for biologists and translational researchers, and a “Model Factory” for machine learning engineers and is built on shared data, models and outputs to enable collaboration across traditionally siloed teams.
Helical said it is already working with multiple top-20 global pharmaceutical companies, including a public collaboration with Pfizer focused on predictive blood-based safety biomarkers. The platform is being used across applications such as target identification, biomarker discovery and therapeutic design, where the company said it has helped compress discovery timelines from years to weeks in certain programs.
The company pointed out the broader pharmaceutical industry continues to grapple with rising costs and low success rates, with R&D spending exceeding $300 billion annually and more than 90% of drug candidates failing in clinical trials. While AI has been widely adopted across the sector, many initiatives remain in pilot stages, with challenges around reproducibility, integration and trust in model outputs.
