Today, Acrobat Genomics (“Acrobat”), a biotechnology company that teaches AI to design proteins using functional data measured directly in human cells, announced the successful completion of Phase I and commencement of expanded research within the Defense Advanced Research Projects Agency (DARPA) Rapid Inhibitor Discovery and Development pipeLine (RIDDL) program as a prime contractor. RIDDL seeks transformative approaches to rapidly discover, design, and develop novel inhibitors of gene editing technologies that could enhance the safety of these technologies and potentially serve as a rapid response to their accidental or intentional misuse.

“We are privileged to work with DARPA to build towards a future where developing rapid responses, whether medical countermeasures or even therapeutics, to emerging threats takes months instead of years. I am particularly proud of what our nimble, focused team has accomplished against the ambitious milestones of this program,” said Nicholas Hughes, PhD, Co-founder/CEO of Acrobat Genomics.

This DARPA program builds on Acrobat’s platform that combines AI-based generative molecular design with high-throughput screening directly within human cells. “Our iterative approach lets us build cutting-edge generation techniques by training our models on our unique datasets. We hope that this work will not only produce novel anti-CRISPR proteins but also provide the basis for designing bespoke inhibitors,” said Muneaki Nakamura, PhD, Co-founder/CSO of Acrobat Genomics.

The company has published peer-reviewed research in Cell Reports Methods validating its approach and has secured additional funding from the National Institutes of Health, leading biopharma companies, and private investors for therapeutic applications.

About Acrobat Genomics

Acrobat Genomics is a Public Benefit Corporation (PBC) focused on biosecurity and therapeutic applications of AI-driven protein design. Founded and led by mission-driven researchers, the company's platform integrates high-throughput functional screening in human cells with machine learning to teach AI to design and optimize novel proteins, compressing discovery timelines from years to months. For more information, please visit https://acrobatgenomics.com/.

Disclaimer Requirement

Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA).

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Unlike most AI approaches, our models learn from hundreds of millions of measurements taken directly in living human cells. This is how we build towards a future of faster and more precise protein therapeutics.