Training Data · Tuesday, August 4, 2026
The field of protein folding and design has significantly evolved with the advent of deep learning, particularly marked by breakthroughs like AlphaFold 2 in 2020. Initially focused on predicting protein structure from amino acid sequences, the field has advanced to designing sequences that fold into specific structures, moving closer to drug design. This evolution was further accelerated by diffusion models, enabling simultaneous generation of protein structures and sequences.
“Um, and like really, it wasn't until 2018 where you start to see this like big step change in in performance, and then finally again in like 2020, uh, with AlphaFold 2.”
“Um, so it started with protein folding. And then, you know, once that became more realizable, uh, there were kind of these other sub problems that people wanted to solve. So now, given a protein structure, can I design a sequence which might fold to that?”
“It was really like the advent of diffusion models where we started to be able like, okay, I can now generate a protein structure and a sequence kind of simultaneously.”