Analysis
BigHat Biosciences announced the completion of a $75 million Series C financing, co-led by DFJ Growth and Premji Invest, according to BioSpace and the company's own release. The round brings BigHat's total funding to $223 million.
The Platform And The Clinical Milestone
BigHat is a clinical-stage, AI-driven protein-therapeutics company that has built what it calls an autonomous AI and experimental platform -- pairing machine-learning models with its own in-house wet-lab experiments to generate proprietary biological data at scale, rather than relying solely on public datasets the way many computational-biology platforms do. That data, in turn, trains the frontier models BigHat uses for therapeutic design.
“That data, in turn, trains the frontier models BigHat uses for therapeutic design.”
The company has already dosed the first patient in a Phase 1 trial evaluating its lead program, BHB810, a CDH17-directed antibody-drug conjugate targeting gastric cancer and other advanced gastrointestinal tumors -- a genuine clinical milestone that separates this round from a pure platform-stage raise.
Who's In The Round
Beyond lead investors DFJ Growth and Premji Invest, the round drew participation from Catalio Capital Management, LG Technology Ventures, Sigmas Group, and existing backers including:
- 8VC and Andreessen Horowitz -- generalist venture firms with deep biotech books
- Amgen Ventures and Eli Lilly's Global Health Innovation Fund -- strategic pharma investors
- Merck's Global Health Innovation Fund and Quadrille Capital -- additional pharma-adjacent and growth backers
- Alexandria Venture Investments, Discovery Ventures, GRIDS Capital, Intermountain Ventures and Section 32 -- existing life-sciences specialists
That breadth of pharma-strategic participation -- Amgen, Lilly and Merck all in the same round -- is a stronger signal of platform credibility than financial investors alone would provide, since strategic pharma investors typically have their own technical diligence teams evaluating the underlying science.
The Competitive Field
BigHat competes for AI-antibody-design credibility against Absci, Generate Biomedicines and AbCellera, all of which similarly pair machine learning with wet-lab validation to design therapeutic proteins. BigHat's differentiation is its emphasis on proprietary, self-generated data rather than licensed or public datasets -- the same own-your-training-data argument Firecrawl makes for web data in this issue's funding section, applied here to biological sequences instead.
No post-money valuation was disclosed alongside this round, which leaves the size of BigHat's actual step-up unclear despite the credible, broad syndicate. For biotech-focused investors, the near-term signal worth tracking is how the BHB810 Phase 1 trial reads out -- an AI-designed antibody-drug conjugate clearing early clinical safety data would be a far stronger proof point for the underlying platform than any funding round size.