BigHat Biosciences, a San Mateo, CA-based biotechnology company developing antibody therapies for patients using machine learning and synthetic biology, raised a $75m Series B funding round.
The round, which brings total funding to date to $100m, was led by Section 32, with participation from new investors Amgen Ventures, Bristol Myers Squibb, Quadrille Capital, Gaingels, GRIDS Capital,as well as prior investors Andreessen Horowitz, 8VC, and AME Cloud Ventures. In conjunction with the funding, Steve Kafka, PhD, Managing Partner of Section 32, joined BigHat ‘s Board. Longtime Alphabet executive and Section 32 Managing Partner, Andy Harrison, co-led this Series B financing and will join as a Board Observer.
The company intends to use the funds to scale the capacity of MillinerTM, an integrated AI/ML- wet lab platform, advance therapeutic programs toward human clinical trials, hire drug discovery and development talent and to accelerate strategic collaborations with flagship partners.
Founded in 2019 by CEO Mark DePristo, a University of Cambridge biochemistry Ph.D., former Head of Genomics for Google.AI, and Co-director of Medical Genetics at Broad Institute, with CSO Peyton Greenside, a Stanford biomedical informatics Ph.D. and 2018 Schmidt Science Fellow, BigHat Biosciences leverages an AI-enabled antibody design platform, Milliner, to design antibody therapies to treat some of the world’s most intractable conditions, from chronic illnesses to life-threatening diseases. At BigHat, every therapeutic program starts with a design blueprint and antibodies generated in its discovery engine or supplied by a partner. These initial molecules are then iteratively transformed into 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 its 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 our design blueprint.
BigHat has active therapeutic programs spanning multiple domains of human health, including inflammation, oncology, and infectious diseases in preclinical studies.