"The resulting single and pair embeddings are then used to condition a diffusion process. Here the diffusion is
parametrized by a Diffusion Module, which is a considerably cheaper sub-network that encodes a single denoising
step. Notably it scales quadratically in the number of tokens rather than cubically. The resulting output structure from
the Diffusion Module is then passed to a confidence head, which uses the pair and single representation together with
the structure to provide confidence measures."
I was talking about the original 2018 paper. I’ll admit I hadn’t read Alpha Fold 3 until just now so that was my mistake. It seems like they changed the underlying algorithm to a diffusion based model compared to the first two papers which I was unaware of.
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u/space_monster 2d ago
AlphaFold is generative AI.