Radical Numerics raises $50M seed to build multimodal AI for biology
What's the deal? Radical Numerics, a startup that teaches AI to read, write, and reason across the full language of biology, has emerged from stealth with a $50M seed round led by Emergence CapitalDealroom has a profile for this one. Try Dealroom →. Obvious Ventures, Triatomic CapitalDealroom has a profile for this one. Try Dealroom →, FactoryDealroom has a profile for this one. Try Dealroom →, and First Spark VenturesDealroom has a profile for this one. Try Dealroom → also participated, while Stripe chief executive officer Patrick Collison backed the company at pre-seed.
The founding team — chief executive officer Eric Nguyen, chief AI scientist Michael Poli, president Stefano Massaroli, and chief technology officer Armin Thomas — are among the researchers who created the field of generative genomics. Three of the four previously built core technology at Liquid AI, an MIT spinout.
Together they built Evo and Evo 2, the first AI models capable of generating DNA at scale, trained on the genomes of more than 100,000 species. Unlike competitors that focus on a single modality — Isomorphic Labs on proteins, Inceptive on RNA — Radical Numerics models DNA, RNA, proteins, and other molecules in a single system.
Why now? Last September, researchers using Evo's open-source weights produced the world's first fully AI-designed functional virus (harmless to humans). That milestone convinced the team their academic work needed a commercial vehicle.
"It still wasn't being picked up in the way we thought it would," Nguyen told Fortune. "So we basically said: we have to show the recipe."
The AI drug discovery market is projected to reach $25B by 2035. Ginkgo Bioworks recently signed a five-year AI platform deal with Google Cloud, and Inceptive inked a deal with Alnylam potentially worth $2B.
What could go wrong? The same models that could accelerate cancer diagnostics could also lower the barrier to designing biological weapons — and Radical Numerics knows it better than anyone, since its own open-source work enabled that first AI-designed genome. "The defence side is sorely losing the race," Nguyen said.
The company brought on Andrew Weber, former US assistant secretary of defence for nuclear, chemical, and biological programmes, as an adviser. Future model releases won't automatically be open-source. The revenue model is also still taking shape — a mix of API licensing, fine-tuned proprietary models for pharma partners, and milestone payments.
"No one has figured out the right business model for how AI companies commercialise in life sciences," Nguyen said. "If anybody says they have a formula, they're just full of it."
The signal: Emergence Capital, best known for backing enterprise SaaS winners, leading a $50M seed in a generative-genomics startup marks a notable expansion into deep-tech life sciences. The round's size — substantial for a seed-stage company with a revenue model still taking shape — reflects investor conviction that multimodal biological AI could become foundational infrastructure for drug discovery, much as large language models have for text, particularly as single-modality competitors like Inceptive are already commanding deals worth up to $2B.
Read more: fortune.com
