Introducing SynthID Bio

Pushmeet Kohli, David Stutz, Ali Cowen-Rivers and Jeremy Ratcliff

Proof of concept for watermarking AI-generated proteins while preserving biological function.

Today, we’re introducing SynthID Bio to bring watermarking technology to synthetic biology. SynthID Bio embeds an imperceptible signature directly into the biological code, ensuring the watermark is verifiable not just on a digital model but on the synthesized, physical protein itself – all while preserving its biological function in laboratory testing.

For protein folding, SynthID Bio fine-tunes a small part of AlphaFold 3’s diffusion network, building the ability to watermark directly into the model’s weights. This ensures that the predicted 3D coordinates inherently carry a detectable signature regardless of who runs the model. SynthID Bio preserves AlphaFold 3 prediction accuracy while offering near-perfect detectability, maintaining key structural feature distributions, and holding up against digital noise or minor coordinate changes.

Realizing the full biosecurity benefits of this work will require community collaboration and further research. As part of our commitment to responsible innovation, we are publishing our methods paper and open-sourcing the code and in vitro data, and releasing the weights to the research community, so we can build on this work. By working openly with partners across biosecurity, gene synthesis, and policy, we can ensure safety and responsibility keeps pace with AI-driven discovery.

We further thank Rudy Bunel, Anna Cupani, Rob Fergus, Thomas Frerix, Sahra Ghalebikesabi, John Jumper, Jacob Kelly, David La, Victor Martin, Sebastian Nowozin, Stig Petersen, Aleks Petrov, Uchechi Okereke, Sylvestre-Alvise Rebuffi, Rosalia Schneider, Armin Senoner, Richard Shuai, Ashok Thillaisundaram, Elisa L. H. Wong, Zachary Wu, and Augustin Žídek for their contributions to the research article, and Julien Bergeron for contributions to the 3D rendering. Finally, we thank Demis Hassabis for his encouragement and support of this project.

Generative AI is helping scientists address critical biological challenges, from predicting the structure of proteins ( AlphaFold ) to designing entirely new proteins ( AlphaProteo , and ProteinMPNN ), and more recently, developing new bacteriophages , viruses that infect bacteria. Yet these tools also present new challenges: novel AI designs can bypass traditional DNA synthesis screening, while mislabeled synthetic 3D structures risk polluting public databases and misleading downstream research.

SynthID Bio is a family of watermarking methods developed specifically for synthetic biology to strengthen biosecurity and scientific integrity.

It adapts its approach depending on the type of data, subtly guiding the choice of amino acids for sequences and adjusting atomic coordinates for predicted 3D structures, creating a reliable signal for detection.