Helical argues that bio foundation models have reached a quality where computational hypothesis-testing is meaningful, but scalable infrastructure remains a bottleneck.

The founders, noting a gap between biological foundation models and verifiable cross-team results, built an application layer to address reproducibility and transferability in pharma work.

Pharma R&D spending is vast, with high costs and failure rates; AI is seen as a path to higher throughput through reproducible, evidence-backed outputs.

Helical is already in production with multiple top-20 pharma companies and has publicly disclosed collaborations, including Pfizer on predictive blood-based safety biomarkers and Tanabe Pharma America on AI-driven target discovery for neurodegenerative diseases like ALS.

As one investor puts it, the system turns model outputs into runnable, defendable workflows, not just the models themselves.

The platform enables collaboration between computational predictions and biological decision-making by using the same data, models, and results across teams to move from hypothesis to decision faster.

The seed round follows an earlier raise of €2.2 million in 2024 from Frst, BoxGroup, and angels who also participated in the current round.

The new funding will broaden deployments with existing and new pharma clients and strengthen the platform’s evidence layer to improve performance across diseases.

Investors describe the shift from siloed AI models to integrated virtual AI labs as biological foundation models converge with general language reasoning.

Notable angel investors include Aidan Gomez, Clement Delangue, and Mario Goetze, expanding support from the AI and tech community.

Helical offers two shared product surfaces—Virtual Lab for biologists and translational scientists and Model Factory for ML engineers and data scientists—enabling shared data and models for auditable results.

Helical was founded in early 2024 by Rick Schneider, Maxime Allard, and Mathieu Klop to bridge fragmentation between model outputs and scientific decisions with an integrated in-silico discovery platform.