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. This research will soon be available as a preprint on bioRvix titled “Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models.” An advanced copy is available here. A similar preprint by Generate Biomedicines is also available here.
