A team led by Baker Lab scientists Joseph Watson, David Juergens, Nate Bennett, Brian Trippe, and Jason Yim has created a powerful new way to design proteins by combining structure prediction networks and generative diffusion models. The team demonstrated extremely high computational success and tested hundreds of A.I.-generated proteins in the lab, finding that many may be useful as medications, vaccines, or even new nanomaterials.The software tool DALL-E produces high-quality images that have never existed before using something called a diffusion model, which is a machine-learning algorithm that specializes in adding and removing noise. Diffusion models for image generation begin with grainy bits of static and gradually remove noise until a clear picture is formed. Additional pieces of software guide this de-noising process so that the new images end up matching what was asked for.

We have developed a guided diffusion model for generating new proteins