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Comment by FrojoS | Hacker News Reader

My understanding is, that it's always 100 new structures, which is a small fraction of the total structures identified in that year.

The reason why the top score in one year, can be lower than in the previous year, is that the test (the 100 structures to guess) is always new and different, so it can end up being 'harder' than the year before. Luck will also play a small role.

Another explanation for a reduction in the top score would be, that previous winners are not re-submitted unchanged. For instance AlphaFold v1 seems to not have been submitted to the latest competition.

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Only 100 new structures each test cycle? That seems a very small test set size ...

Is it really possible to select 100 new structures which together are likely to represent a meaningful increase in the sample generalization versus the prior years test set ...?

Given that we only know the structure of on the order of 100k proteins, we might only get another 10k new ones per year. I guess.

Using 1% of those (presumably from the more-often-reproduced subset) for this challenge seems reasonable? Note that the structures have to remain secret up until the challenge, and presumably all those teams uncovering the structures don't want to have to wait up to 2 years every time to actually make their results public.

Interesting ... plenty of opportunity then potentially for the 100 samples to have prediction similarity to the set of published discoveries (for expected or unknown reasons)?

I suppose it will take a few more years of repetition for the challenge to confirm that the problem has been been solved -- but I wonder if a new version of the contest is going to be needed as well? Maybe the model accuracy is now high enough to invert the contest to a form where models generate predictions for randomly selected unknown samples -- and experimental teams are then expected to make observations for those particular sequences over the next two years as part of their otherwise research agenda selected experimental workload?

There are different categories of samples, namely FM and TBM targets. FM targets don't have any similarity to known structures. Roughly a quarter were FM targets. I think the more interesting thing to look at is the size of the multiple sequence alignments (MSAs) which is the basis of this and essentially all methods. They seem to do very well with few MSAs, which bodes well for other targets, although there are families of proteins with few MSAs.

100 structure with 100+ amino acids each, so it's not quite as bad. Part of the folding information is contained within a distance of a few amino acids, while some (the harder part and crux of the problem) is farther away.

But yeah, compared to other fields, the size of training/test sets is sometimes pretty small in ML for life sciences.

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