I don’t think you can say DeepMind could ever be more accurate to the true physical structure since it was built on the same experimental structures that it is being compared to. The limit of accuracy is the experimental data. However, I think we can say that a DeepMind prediction could at least be as good as a new experimental structure.
This seems like an obvious assumption to make, but it isnt always true. It is easier to see why if you are measuring a single value multiple times in order to get a more accurate estimate of the true value. In that case your "model" is simply the mean of all measurements made and can exceed the accuracy of a single measurement.
In this case, the model is predicting values of multiple structures, but patterns could still theoretically be found which allow for predictions beyond the accuracy of a single measurement.
DM is merging several experimental data: known x-ray structures, and evolutionary data. The experimental method (xray) doesn't take advantage of the evolutionary data. And it also doesn't model the underlying protein behavior accurately (xray basically assumes a single static model with atoms fluctuating in little gaussian "puffs" around the atomic centers, but that's not how most proteins behave).
But DeepMind could be used to find errors in the training set.
Let’s say you have 100000 proteins in the training set. Now remove #1 and train on 99999, and then check that it still predicts the same protein result for #1 as the experimental result.
Or remove from training whole sets of proteins by particular teams to find systematic errors made by teams?
Is that true? I thought fundamentally, the simulation tries to find the state of lowest energy, which is defined by physics. So, your result can be better than the data set used for training.
