{ "id": 577878, "title": "Publication round up", "url": "/competitions/stanford-rna-3d-folding/discussion/577878", "author_name": "", "post_date": "2025-05-07T16:26:59.836071300Z", "votes": 6, "comment_count": 1, "views": 0, "content": "

I've posted 3 topics with links to recent papers, and I thought it would be useful to bring them all together in one post. Here's the list and links, plus other links from other posts. If I have time, I'll go through and curate them to remove duplicates.

\n

Happy reading!

\n

My posted papers

\n

Limits of deep-learning-based RNA prediction methods
\nCritical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game
\ntrRosettaRNA: automated prediction of RNA 3D structure with transformer network. Wang, W. et al. Nature Commun. 14, 7266 (2023).
\nAccurate RNA 3D structure prediction using a language model-based deep learning approach. Shen, T. et al. Nature Methods 21, 2287–2298 (2024).
\nDeep generative design of RNA aptamers using structural predictions. Wong, F. et al. Nature Comput. Sci. 4, 829–839 (2024).
\nDeep generative design of RNA family sequences. Sumi, S., Hamada, M. & Saito, H. Nature Methods 21, 435–443 (2024).
\nRNA language models predict mutations that improve RNA function. Shulgina, Y. et al. Nature Commun. 15, 10627 (2024).
\nATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data. Boyd, N. et al. BioRxiv (2023)
\nRibonanza: deep learning of RNA structure through dual crowdsourcing. He, S. et al. BioRxiv (2024).

\n

From @ravi20076's original starting materials post

\n

Reading materials
\nhttps://www.sciencedirect.com/science/article/abs/pii/S0022283699930012
\nhttps://www.annualreviews.org/content/journals/10.1146/annurev.biophys.26.1.113
\nhttps://www.sciencedirect.com/science/article/pii/S0021925818902863
\nhttps://www.nature.com/articles/nrm1497
\nhttps://www.annualreviews.org/content/journals/10.1146/annurev.biophys.34.040204.144511
\nhttps://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=RNA+folding&btnG=
\nhttps://academic.oup.com/nar/article/13/5/1717/1035042
\nhttps://www.cell.com/trends/biochemical-sciences/fulltext/S0968-0004(96)80169-1
\nhttps://www.sciencedirect.com/science/article/abs/pii/S0959440X03000666
\nhttps://www.sciencedirect.com/science/article/abs/pii/S0959440X02003251

\n

Another great set of papers

\n

from @ahsuna123.

\n
\n
    \n
  1. Ribonanza: deep learning of RNA structure through dual crowdsourcing.
    2. \n
  2. gRNAde: Geometric Deep Learning for 3D RNA inverse design
    3. \n
  3. A Method to Predict the 3D Structure of an RNA Scaffold
    4. \n
  4. trRosettaRNA: automated prediction of RNA 3D structure with transformer network
    5. \n
  5. Accurate RNA 3D structure prediction using a language model-based deep learning approach\n
    6. \n
  6. Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks
    7. \n
  7. RNA 3D Structure Prediction: Progress and Perspective
    8. \n
  8. RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling
    9. \n
  9. Predicting the 3D Structure of RNA from Sequence
    10. \n
  10. Advances in the field of RNA 3D structure prediction and modeling, with purely theoretical approaches, and with the use of experimental data\n
    11. \n
\n
\n

Fantastic starter pack of papers, models, etc.) from @kalilurrahman

\n

Late additions

\n

How could I forget the paper posted by the orgnizers? Thoughts on how to think (and talk) about RNA structure

\n

And the amazing Medium Level Introduction to RNA folding by @tilii7

", "messages": [ { "id": "3196934", "postDate": "05/07/2025 16:26:59", "content": "

I've posted 3 topics with links to recent papers, and I thought it would be useful to bring them all together in one post. Here's the list and links, plus other links from other posts. If I have time, I'll go through and curate them to remove duplicates.

\n

Happy reading!

\n

My posted papers

\n

Limits of deep-learning-based RNA prediction methods
\nCritical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game
\ntrRosettaRNA: automated prediction of RNA 3D structure with transformer network. Wang, W. et al. Nature Commun. 14, 7266 (2023).
\nAccurate RNA 3D structure prediction using a language model-based deep learning approach. Shen, T. et al. Nature Methods 21, 2287–2298 (2024).
\nDeep generative design of RNA aptamers using structural predictions. Wong, F. et al. Nature Comput. Sci. 4, 829–839 (2024).
\nDeep generative design of RNA family sequences. Sumi, S., Hamada, M. & Saito, H. Nature Methods 21, 435–443 (2024).
\nRNA language models predict mutations that improve RNA function. Shulgina, Y. et al. Nature Commun. 15, 10627 (2024).
\nATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data. Boyd, N. et al. BioRxiv (2023)
\nRibonanza: deep learning of RNA structure through dual crowdsourcing. He, S. et al. BioRxiv (2024).

\n

From @ravi20076's original starting materials post

\n

Reading materials
\nhttps://www.sciencedirect.com/science/article/abs/pii/S0022283699930012
\nhttps://www.annualreviews.org/content/journals/10.1146/annurev.biophys.26.1.113
\nhttps://www.sciencedirect.com/science/article/pii/S0021925818902863
\nhttps://www.nature.com/articles/nrm1497
\nhttps://www.annualreviews.org/content/journals/10.1146/annurev.biophys.34.040204.144511
\nhttps://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=RNA+folding&btnG=
\nhttps://academic.oup.com/nar/article/13/5/1717/1035042
\nhttps://www.cell.com/trends/biochemical-sciences/fulltext/S0968-0004(96)80169-1
\nhttps://www.sciencedirect.com/science/article/abs/pii/S0959440X03000666
\nhttps://www.sciencedirect.com/science/article/abs/pii/S0959440X02003251

\n

Another great set of papers

\n

from @ahsuna123.

\n
\n
    \n
  1. Ribonanza: deep learning of RNA structure through dual crowdsourcing.
    2. \n
  2. gRNAde: Geometric Deep Learning for 3D RNA inverse design
    3. \n
  3. A Method to Predict the 3D Structure of an RNA Scaffold
    4. \n
  4. trRosettaRNA: automated prediction of RNA 3D structure with transformer network
    5. \n
  5. Accurate RNA 3D structure prediction using a language model-based deep learning approach\n
    6. \n
  6. Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks
    7. \n
  7. RNA 3D Structure Prediction: Progress and Perspective
    8. \n
  8. RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling
    9. \n
  9. Predicting the 3D Structure of RNA from Sequence
    10. \n
  10. Advances in the field of RNA 3D structure prediction and modeling, with purely theoretical approaches, and with the use of experimental data\n
    11. \n
\n
\n

Fantastic starter pack of papers, models, etc.) from @kalilurrahman

\n

Late additions

\n

How could I forget the paper posted by the orgnizers? Thoughts on how to think (and talk) about RNA structure

\n

And the amazing Medium Level Introduction to RNA folding by @tilii7

", "rawMarkdown": "I've posted 3 topics with links to recent papers, and I thought it would be useful to bring them all together in one post. Here's the list and links, plus other links from other posts. If I have time, I'll go through and curate them to remove duplicates.\n\nHappy reading!\n\n# My posted papers\n\n[Limits of deep-learning-based RNA prediction methods](https://www.biorxiv.org/content/10.1101/2025.04.30.651414v1)\n[Critical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game] (https://pubs.acs.org/doi/10.1021/acs.jcim.5c00245)\n[trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Wang, W. et al. Nature Commun. 14, 7266 (2023).](https://doi.org/10.1038%2Fs41467-023-42528-4)\n[Accurate RNA 3D structure prediction using a language model-based deep learning approach. Shen, T. et al. Nature Methods 21, 2287–2298 (2024).](https://doi.org/10.1038%2Fs41592-024-02487-0)\n[Deep generative design of RNA aptamers using structural predictions. Wong, F. et al. Nature Comput. Sci. 4, 829–839 (2024).](https://doi.org/10.1038%2Fs43588-024-00720-6)\n[Deep generative design of RNA family sequences. Sumi, S., Hamada, M. & Saito, H. Nature Methods 21, 435–443 (2024).](https://doi.org/10.1038%2Fs41592-023-02148-8)\n[RNA language models predict mutations that improve RNA function. Shulgina, Y. et al. Nature Commun. 15, 10627 (2024).](https://doi.org/10.1038%2Fs41467-024-54812-y)\n[ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data. Boyd, N. et al. BioRxiv (2023)](https://doi.org/10.1101/2023.12.13.571579)\n[Ribonanza: deep learning of RNA structure through dual crowdsourcing. He, S. et al. BioRxiv (2024)](https://doi.org/10.1101/2024.02.24.581671).\n\n# From @ravi20076's [original starting materials post](https://www.kaggle.com/competitions/stanford-rna-3d-folding/discussion/565292)\n\n\n\nReading materials\nhttps://www.sciencedirect.com/science/article/abs/pii/S0022283699930012\nhttps://www.annualreviews.org/content/journals/10.1146/annurev.biophys.26.1.113\nhttps://www.sciencedirect.com/science/article/pii/S0021925818902863\nhttps://www.nature.com/articles/nrm1497\nhttps://www.annualreviews.org/content/journals/10.1146/annurev.biophys.34.040204.144511\nhttps://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=RNA+folding&btnG=\nhttps://academic.oup.com/nar/article/13/5/1717/1035042\nhttps://www.cell.com/trends/biochemical-sciences/fulltext/S0968-0004(96)80169-1\nhttps://www.sciencedirect.com/science/article/abs/pii/S0959440X03000666\nhttps://www.sciencedirect.com/science/article/abs/pii/S0959440X02003251\n\n# [Another great set of papers](https://www.kaggle.com/competitions/stanford-rna-3d-folding/discussion/565426)\n from @ahsuna123.\n> 1. [Ribonanza: deep learning of RNA structure through dual crowdsourcing](https://pmc.ncbi.nlm.nih.gov/articles/PMC10925082/).\n> 2. [gRNAde: Geometric Deep Learning for 3D RNA inverse design](https://arxiv.org/html/2305.14749v4)\n> 3. [A Method to Predict the 3D Structure of an RNA Scaffold](https://pmc.ncbi.nlm.nih.gov/articles/PMC5512611/#:~:text=To%20predict%20RNA%203D%20structures,predicts%20the%20corresponding%203D%20structures.)\n> 4. [trRosettaRNA: automated prediction of RNA 3D structure with transformer network](https://www.nature.com/articles/s41467-023-42528-4)\n> 5. [Accurate RNA 3D structure prediction using a language model-based deep learning approach\n> ](https://www.nature.com/articles/s41592-024-02487-0)\n> 6. [Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks](https://pubmed.ncbi.nlm.nih.gov/37838833/)\n> 7. [RNA 3D Structure Prediction: Progress and Perspective](https://www.mdpi.com/1420-3049/28/14/5532)\n> 8. [RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling ](https://academic.oup.com/nar/article/51/7/3341/7067938)\n> 9. [Predicting the 3D Structure of RNA from Sequence](https://link.springer.com/referenceworkentry/10.1007/978-981-16-1313-5_14-1)\n> 10. [Advances in the field of RNA 3D structure prediction and modeling, with purely theoretical approaches, and with the use of experimental data\n> ](https://www.cell.com/structure/fulltext/S0969-2126(24)00332-0)\n\n# [Fantastic starter pack of papers, models, etc.](https://www.kaggle.com/competitions/stanford-rna-3d-folding/discussion/567046)) from @kalilurrahman \n\n# Late additions\n\nHow could I forget the paper posted by the orgnizers? [Thoughts on how to think (and talk) about RNA structure](https://www.pnas.org/doi/10.1073/pnas.2112677119)\n\nAnd the amazing [Medium Level Introduction to RNA folding](https://www.kaggle.com/competitions/stanford-rna-3d-folding/discussion/568445) by @tilii7", "votes": null }, { "id": "3197530", "postDate": "05/08/2025 08:49:22", "content": "

Thanks for sharing, this provides beginners with immediate access to the topic. Wishing everyone a successful outcome!

", "rawMarkdown": "Thanks for sharing, this provides beginners with immediate access to the topic. Wishing everyone a successful outcome!", "votes": null } ], "comments": [ { "id": 3197530, "author_name": "nigmatrahim4", "author_url": "", "post_date": "05/08/2025 08:49:22", "content": "

Thanks for sharing, this provides beginners with immediate access to the topic. Wishing everyone a successful outcome!

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3196934": "I've posted 3 topics with links to recent papers, and I thought it would be useful to bring them all together in one post. Here's the list and links, plus other links from other posts. If I have time, I'll go through and curate them to remove duplicates.\n\nHappy reading!\n\n# My posted papers\n\n[Limits of deep-learning-based RNA prediction methods](https://www.biorxiv.org/content/10.1101/2025.04.30.651414v1)\n[Critical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game] (https://pubs.acs.org/doi/10.1021/acs.jcim.5c00245)\n[trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Wang, W. et al. Nature Commun. 14, 7266 (2023).](https://doi.org/10.1038%2Fs41467-023-42528-4)\n[Accurate RNA 3D structure prediction using a language model-based deep learning approach. Shen, T. et al. Nature Methods 21, 2287–2298 (2024).](https://doi.org/10.1038%2Fs41592-024-02487-0)\n[Deep generative design of RNA aptamers using structural predictions. Wong, F. et al. Nature Comput. Sci. 4, 829–839 (2024).](https://doi.org/10.1038%2Fs43588-024-00720-6)\n[Deep generative design of RNA family sequences. Sumi, S., Hamada, M. & Saito, H. Nature Methods 21, 435–443 (2024).](https://doi.org/10.1038%2Fs41592-023-02148-8)\n[RNA language models predict mutations that improve RNA function. Shulgina, Y. et al. Nature Commun. 15, 10627 (2024).](https://doi.org/10.1038%2Fs41467-024-54812-y)\n[ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data. Boyd, N. et al. BioRxiv (2023)](https://doi.org/10.1101/2023.12.13.571579)\n[Ribonanza: deep learning of RNA structure through dual crowdsourcing. He, S. et al. BioRxiv (2024)](https://doi.org/10.1101/2024.02.24.581671).\n\n# From @ravi20076's [original starting materials post](https://www.kaggle.com/competitions/stanford-rna-3d-folding/discussion/565292)\n\n\n\nReading materials\nhttps://www.sciencedirect.com/science/article/abs/pii/S0022283699930012\nhttps://www.annualreviews.org/content/journals/10.1146/annurev.biophys.26.1.113\nhttps://www.sciencedirect.com/science/article/pii/S0021925818902863\nhttps://www.nature.com/articles/nrm1497\nhttps://www.annualreviews.org/content/journals/10.1146/annurev.biophys.34.040204.144511\nhttps://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=RNA+folding&btnG=\nhttps://academic.oup.com/nar/article/13/5/1717/1035042\nhttps://www.cell.com/trends/biochemical-sciences/fulltext/S0968-0004(96)80169-1\nhttps://www.sciencedirect.com/science/article/abs/pii/S0959440X03000666\nhttps://www.sciencedirect.com/science/article/abs/pii/S0959440X02003251\n\n# [Another great set of papers](https://www.kaggle.com/competitions/stanford-rna-3d-folding/discussion/565426)\n from @ahsuna123.\n> 1. [Ribonanza: deep learning of RNA structure through dual crowdsourcing](https://pmc.ncbi.nlm.nih.gov/articles/PMC10925082/).\n> 2. [gRNAde: Geometric Deep Learning for 3D RNA inverse design](https://arxiv.org/html/2305.14749v4)\n> 3. [A Method to Predict the 3D Structure of an RNA Scaffold](https://pmc.ncbi.nlm.nih.gov/articles/PMC5512611/#:~:text=To%20predict%20RNA%203D%20structures,predicts%20the%20corresponding%203D%20structures.)\n> 4. [trRosettaRNA: automated prediction of RNA 3D structure with transformer network](https://www.nature.com/articles/s41467-023-42528-4)\n> 5. [Accurate RNA 3D structure prediction using a language model-based deep learning approach\n> ](https://www.nature.com/articles/s41592-024-02487-0)\n> 6. [Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks](https://pubmed.ncbi.nlm.nih.gov/37838833/)\n> 7. [RNA 3D Structure Prediction: Progress and Perspective](https://www.mdpi.com/1420-3049/28/14/5532)\n> 8. [RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling ](https://academic.oup.com/nar/article/51/7/3341/7067938)\n> 9. [Predicting the 3D Structure of RNA from Sequence](https://link.springer.com/referenceworkentry/10.1007/978-981-16-1313-5_14-1)\n> 10. [Advances in the field of RNA 3D structure prediction and modeling, with purely theoretical approaches, and with the use of experimental data\n> ](https://www.cell.com/structure/fulltext/S0969-2126(24)00332-0)\n\n# [Fantastic starter pack of papers, models, etc.](https://www.kaggle.com/competitions/stanford-rna-3d-folding/discussion/567046)) from @kalilurrahman \n\n# Late additions\n\nHow could I forget the paper posted by the orgnizers? [Thoughts on how to think (and talk) about RNA structure](https://www.pnas.org/doi/10.1073/pnas.2112677119)\n\nAnd the amazing [Medium Level Introduction to RNA folding](https://www.kaggle.com/competitions/stanford-rna-3d-folding/discussion/568445) by @tilii7", "3197530": "Thanks for sharing, this provides beginners with immediate access to the topic. Wishing everyone a successful outcome!" }, "source": "meta" }