RNet has been enabled in puzzlemaker on the eternadev.org site for players to create RNet puzzles and assess how solvable those puzzles are. We would like to avoid a repeat of EFTK where players got frustrated by how difficult and illogical the EFTK player puzzles are. If you don’t have a dev account, you will need to create one. You can use the same player name and password as your main account for ease if you like.
There is no energy calculation in RNet and instead it provides an F1 score. View the F1 score by opening the spec box and keeping it open while flipping. (Box can be resized by dragging the corners.) If F1 proves unhelpful, then I can report to the researchers that RNet in the game would benefit from another metric for puzzle solving.
I recommend we keep puzzles under 300 bases to start because the RNet calculations become slower as the puzzles get longer. It will be great to see lots of pseudoknot puzzles, but non-pseudoknot puzzles also are good for testing. The puzzles on the dev site are for testing; they may not persist in the future. If you want to keep the structures for later use, keep a record for yourself.
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Here are some observations I have made while making RNet puzzles:
There are no energies, though you can tell bulges, and all loops are boostable.
Interesting a GU on 80;71 forms this PK on 24;120. Another pic shows a GC unbinding in a stem.
215715_1748316981.png (1707×735)
This Rnet puzzle is called Rnet Study 3.
Interesting, it seems like since it’s a ML model, instead of building up the pairing probabilities, it starts with almost everything being able to pair with something else (albeit with <0.1 confidence), and as you add more pairs, it removes possible bonds until only your intended structure can form. It seems to like structures like <…(… …)…> a lot more than other folding engines, same with pks. It looks a lot like other AI models trained on a somewhat small dataset, where when given a really unlikely case, they give a really unlikely output. It’s a really interesting folding engine.
Thanks for the feedback! Can you post the RNet puzzle with that structure on the dev site? RNet was trained on >500,000 RNA sequences. Is that considered a small dataset?
Interesting, since simpler AI training sets like MNIST (https://en.wikipedia.org/wiki/MNIST_database) only have 40k training and 10k test, albeit they are simpler. While Rnet might have a different type of model to these AIs, since these have fixed input and output spaces, over 500k is a respectable size for a training set. I assume that’s the training data, not the test data, so it is interesting to see it create wildly impossible pseudoknots on a string of Adenines with only a few Uracils, Guanines, and Cystonines. I’ll look into it more, however, and post the unlikely sequences on the dev site soon 
Puzzle is posted 
, here’s the seq if anyone needs it (they probably will): AAAAAAAAUAAAACAAAAUAAAAAAAAAACUUUUAAAAAAAAAAAAAAAAAAAAAAAAGUGACAAAUAAACAAAAAAAAAAAAGCGAAAAAAAAGAAAAAA
To set RibonanzaNet-SS in context for newer players, RNet is far from perfect. RNet 2 has been released, if anyone would like to try it on the Kaggle site. No accurate RNA folding prediction model exists. That is one of the primary reasons Eterna exists, to help researchers develop better models. I personally don’t think a 2D model, such as Vienna 2, could ever be usably accurate without taking 3D contacts into account. Which is precisely what we are trying to accomplish with RNet.
