I mean, yes, but it's similar to how image segmentation entails image classification (if you can segment the dog you can classify the image as one containing a dog).

There's oodles of labelled images of dogs, but comparably much fewer datasets of dog silhouettes.

Another factor is that it's far easier (and less informative) to predict that two proteins are capable of interacting with any degree of affinity than with a specific amount.

You may say to yourself (as I once did): "Well surely a well calibrated PPI inference model will output interaction probabilities that correlate with binding affinity!"

I've tested this and I've yet to find one written by myself or others that behaves this way.

If this line of questioning is interesting to, definitely sign up for Google Scholar alerts on my name because we're publishing some very cool stuff on precisely this v. soon.

Really interesting point about the non-correlation of affinity and PPI probabilities. Disappointing, honestly. Mechanistic systems bio models would really benefit from large scale affinity information, and it would be pretty cool if ML methods trained on binary PPI data learned a kind of latent affinity model. Maybe PPI models could be fine tuned to do that? Or maybe there’s specific neurons in the PPI models that correlate better with affinity, similar to the mechanistic interpretability stuff that people are doing on LLM’s? I only follow this area tangentially so I imagine I’m not the first person to have those ideas…

Anyways I look forward to seeing what you publish!