Super excited to share our year-long work on designing proteins directly from natural language, led by @jleechung in collaboration with @UWproteindesign @allen_ai! Current protein design models are constrained in what they can condition on. As an analogy, think of an image generation model that only conditions on discrete class labels like "dog" or "cat", or "outdoors" or "indoors"; in contrast, a modern text-to-image model can be much more expressively controlled. We do the same for protein design here, and the resulting model natively turns natural instructions into corresponding protein structures. This makes it much more effective at designing proteins with particular form/function, and it works especially well for agent-driven protein design!

✨Introducing Prompteina: all-atom protein design directly from natural language✨ - Control diverse protein properties through a single text prompt - Higher predicted binding confidence than baselines - First experimentally validated de novo binders reported for GM2A 🧵