BigHat Biosciences raises $75M to design safer, more effective antibodies

shutterstock antibody therapies

A company developing safer, more effective antibody therapies for patients using machine learning and synthetic biology, today (July 20) announced it has raised $75 million in funding.

BigHat Biosciences’ series B funding round was led by Section 32 with participation from new investors Amgen Ventures, Bristol Myers Squibb, Quadrille Capital, Gaingels, GRIDS Capital and others.

The company’s funding has now reached $100 million and will be used to scale the capacity of Milliner, an integrated artificial intelligence (AI) and machine learning (ML) wet lab platform, as well as to advance therapeutic programs toward human clinical trials, aggressively hire drug discovery and development talent, and accelerate strategic collaborations with flagship partners.


More than 200 antibodies and other biotherapeutics are approved today as part of a global biologics market expected to grow to $421 billion by 2025. Next generation antibody therapies promise improved safety and efficacy for patients.

The company said developing these advanced molecules can be difficult, costly, and slow with conventional techniques, but that its AI-enabled antibody design platform, Milliner, offers the technologies to quickly and reliably create these breakthrough therapies.

BigHat said it will use Milliner to design safer, more effective antibody therapies to treat some of the world’s most intractable conditions, from chronic illnesses to life-threatening diseases.

The company said it starts every therapeutic program with a design blueprint and antibodies generated in its discovery engine or supplied by a partner.

Machine learning

Then the initial molecules are transformed into next-generation therapies on the Milliner platform through sequential design-build-test cycles. BigHat’s machine learning models design hundreds of variants that are built and tested in the lab using the latest synthetic biology technologies in each cycle.

These measurements include biophysical properties and impact on disease activity for every variant using cell-based or other functional assays that replicate in vivo disease processes. This new data is used to update the AI/ML models so that over multiple cycles, these models learn to create antibodies that match the design blueprint.

Steve Kafka, managing partner of Section 32 and newly-appointed BigHat board member, said: “BigHat is ushering the next wave of personalized medicines with a sophisticated AI platform integrated with a next-generation lab that addresses the complexities and inefficiencies associated with biologics discovery.”

Philip Tagari, vice president of research at Amgen, said: “Completion of the first stage of Amgen’s research collaboration with BigHat demonstrated the ability of their platform to quickly and significantly optimize next-generation single-domain antibodies, validating the platform as a path to generating target binders with improved properties compared with the original repertoire identified by traditional technologies. We’re excited to participate in the funding round to assist in the continued development and deployment of BigHat’s approach.”

The company was founded in 2019 by Mark DePristo, from the University of Cambridge, with chief scientific officer, Peyton Greenside. Since that time, BigHat has reached business and scientific milestones.


In addition to its $5 million seed and $19 million series A rounds, BigHat was awarded a Small Business Innovation Research (SBIR) grant from the National Institute of Standards and Technology (NIST). The company received Amgen’s 2021 Golden Ticket to MBC BioLabs, was named to Business Insider’s 24 top biotech companies list, awarded the UCSF Health Award and named Therapeutics Innovation of the Year by the BioTech Breakthrough Awards.

DePristo said: “The scale and flexibility of the BigHat platform allows us to partner with companies anywhere along the therapeutic discovery continuum as well as pursue internal therapeutics programs to address major unmet needs across a variety of diseases.

“Combining internal programs and partnerships enables us to bring as many assets to market as quickly as possible. All while capturing a wealth of data to continuously improve our AI/ML models and wet lab techniques, making future campaigns faster and better. The BigHat team is excited and ready to put this Series B funding to excellent use.”

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