What i take away is the simplicity and scaling behavior. The ML field often sees an increase in module complexity to reach higher scores, and then a breakthrough where a simple model performs on-par with the most complex. That such a "simple" architecture works this well on its own, means we can potentially add back the complexity again to reach further. Can we add back MSA now? where will that take us?

My rough understanding of field is that a "rough" generative model makes a bunch of decent guesses, and more formal "verifiers" ensure they abide by the laws of physics and geometry. The AI reduce the unfathomably large search-space so the expensive simulation doesn't need to do so much wasted work on dead-ends. If the guessing network improves, then the whole process speeds up.

- I'm recalling the increasingly complex transfer functions in redcurrant networks,

- The deep pre-processing chains before skip forward layers.

- The complex normalization objectives before Relu.

- The convoluted multi-objective GAN networks before diffusion.

- The complex multi-pass models before full-convolution networks.

So basically, i'm very excited by this. Not because this itself is an optimal architecture, but precisely because it isn't!

> Can we add back MSA now?

Using MSAs might be a local optimum. ESM showed good performance on some protein problems without MSAs. MSAs offer a nice inductive bias and better average performance. However, the cost is doing poorly on proteins where MSAs are not accurate. These include B and T cell receptors, which are clinically very relevant.

Isomorphic Labs, Oxford, MRC, and others have started the OpenBind Consortium (https://openbind.uk) to generate large-scale structure and affinity data. I believe that once more data is available, MSAs will be less relevant as model inputs. They are "too linear".