Researchers use generative AI to design novel proteins

Researchers at the University of Toronto have developed an artificial intelligence system that can create proteins not found in nature using generative diffusion, the same technology behind popular image-creation platforms such as DALL-E and Midjourney.

The system will help advance the field of generative biology, which promises to speed drug development by making the design and testing of entirely new therapeutic proteins more efficient and flexible.

"Our model learns from image representations to generate fully new proteins, at a very high rate," says Philip M. Kim, a professor in the Donnelly Centre for Cellular and Biomolecular Research at U of T's Temerty Faculty of Medicine. "All our proteins appear to be biophysically real, meaning they fold into configurations that enable them to carry out specific functions within cells."

The new system, which the researchers call ProteinSGM, draws from a large set of image-like representations of existing proteins that encode their structure accurately. The researchers feed these images into a generative diffusion model, which gradually adds noise until each image becomes all noise.

The model tracks how the images become noisier and then runs the process in reverse, learning how to transform random pixels into clear images that correspond to fully novel proteins.

Image source: Kim et al. (2023), Nature Computational Science.