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The larger change is in how coordinates are produced. Rather than a Structure Module built on rotations and translations, AlphaFold3 uses a diffusion module that operates directly on raw atom coordinates and a coarse token representation, without rotational frames at all. Diffusion models are trained on a simple, general task: take a true data point, corrupt it with noise at a known level, and train a network to recover the original from the corrupted version. For coordinates $x_0$ noised to $x_t$ at noise level t , $x_t = \sqrt{\bar{\alpha}_t}\, x_0 + \sqrt{1-\bar{\alpha}_t}\, \epsilon, \qquad \hat{x}_0 = f_\theta(x_t, t)$. with $\epsilon$ drawn from a standard normal distribution and $f_\theta$ the trained network. Generating a structure then means starting from coordinates that are almost pure noise and repeatedly applying the trained denoiser, each pass nudging the atoms closer to a physically coherent arrangement, across many scales at once — from the local geometry of a single ring to the global fold of an entire complex [ 2 ] . Both AlphaFold2 and AlphaFold3 were trained on structures deposited in the Protein Data Bank; AlphaFold3’s training data was cut at structures released before 30 September 2021, supplemented by sequence databases including UniRef90, Uniclust30, and BFD, and RNA-specific databases including Rfam and RNAcentral [ 2 ] .