Training Data · Tuesday, August 4, 2026
Diffusion models have been a key breakthrough for generative AI in biology, enabling the generation of reasonable-looking protein structures and sequences. Unlike previous methods like variational autoencoders, diffusion models allow the model more time to iteratively refine outputs, making small improvements step-by-step. This approach has proven highly effective for biological problems, allowing for the generation of protein sequences that fold into desired structures with specific properties.
“It was pretty crazy. Like there were a bunch of generative modeling approaches that like could work if if you had a bunch of data.”
“So it was really like once diffusion models came around, and Once, um, you know, we were able to generate sequences, uh, you know, that would fold into these protein structures, then the entire industry started to see the power.”
“What turned out working really well was just kind of giving the model more time to think and showing it like more examples like, here's like a slightly broken looking protein, how do you make it better?”