California-Based BigHat Biosciences Raises $75M in Series C Financing

The round also saw participation from Catalio Capital Management, LG Technology Ventures, Sigmas Group, and existing investors including 8VC, Alexandria Venture Investments, Amgen Ventures, Andreessen Horowitz (a16z), Discovery Ventures, GRIDS Capital, Intermountain Ventures, Eli Lilly and Company, Merck Global Health Innovation Fund, Quadrille Capital, and Section 32.

The company will use the funds to expand its data generation platform for protein design and advance its AI-designed therapeutics, including BHB810 and BHB299, toward key clinical readouts.

What is BigHat Biosciences?

BigHat Biosciences is an AI-native biotechnology company developing protein-based medicines.

Its platform combines AI with autonomous, high-throughput laboratory experiments to generate biological data at scale. This data is used to improve AI models for protein design and support the development of new therapeutics.

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BigHat applies its technology across different protein engineering challenges, therapeutic areas, and molecular design objectives. The platform creates a continuous process in which AI designs molecules and laboratory experiments test and improve those designs.

How Much Funding Has BigHat Biosciences Raised?

BigHat Biosciences has raised $223 million in total funding to date.

Its latest financing was a $75 million Series C round co-led by DFJ Growth and Premji Invest.

The source does not provide details about the company's previous funding rounds or their dates.

Who Invested in BigHat Biosciences’ Series C?

The Series C round was co-led by DFJ Growth and Premji Invest.

New participating investors included Catalio Capital Management, LG Technology Ventures, and Sigmas Group.

Existing investors participating in the round included 8VC, Alexandria Venture Investments, Amgen Ventures, Andreessen Horowitz (a16z), Discovery Ventures, GRIDS Capital, Intermountain Ventures, Eli Lilly and Company, Merck Global Health Innovation Fund, Quadrille Capital, and Section 32.

Why the Funding Matters

BigHat will use the new capital to expand its platform for rapid biological data generation and support the development of its protein design capabilities.

The funding will also help advance its pipeline of AI-designed therapeutics, including BHB810 and BHB299, toward key clinical readouts.

Who founded BigHat Biosciences?

BigHat Biosciences was founded in 2019 by Peyton Greenside.

The company is headquartered in San Mateo, California.

Leadership Comments

“Our mission is to design transformative therapeutics by combining frontier AI with the autonomous generation of gold-standard biological data,” said Peyton Greenside, co-founder and CEO of BigHat. “This financing will help us advance both sides of that mission: our pipeline of BigHat-designed therapeutics, beginning with BHB810, one of the first AI-designed biologics to reach the clinic, and our leading platform for AI-designed biologics.

“BigHat has uniquely enabled reinforcement learning for molecular design with a platform optimized for industry-leading speed, at the scale today's frontier models need,” said Justin Kao, Partner at DFJ Growth. “This integrated learning loop is a powerful advantage, enabling BigHat to rapidly design and optimize therapeutics with the properties needed for clinical development.

Market and Business Growth

BigHat has applied its AI and experimental platform across its internal drug development pipeline and external partnerships.

The company has used its platform across multiple therapeutic areas, protein modalities, and molecular design objectives while generating biological datasets used to train and improve AI models.

Its platform combines AI-driven molecular design with autonomous experimentation, creating a continuous learning process between protein design and experimental validation.

What Happens Next?

BigHat plans to continue expanding its biological data generation platform and advancing its AI-designed therapeutics.

The company will focus on moving BHB810 and BHB299 toward key clinical readouts while continuing to develop its protein design capabilities.

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