{
  "id": 668412,
  "title": "🧬 RNAPro - Inference Pipeline",
  "url": "/competitions/stanford-rna-3d-folding-2/discussion/668412",
  "author_name": "Theo Viel",
  "post_date": "2026-01-16T15:35:04.804000",
  "votes": 26,
  "comment_count": 7,
  "views": 0,
  "content": "<p>We’re excited to share an inference workflow for RNAPro for part 2 of the Stanford RNA 3D Folding competition ! RNAPro reflects where the field landed after last year’s breakthroughs. </p>\n<h2>What is RNAPro?</h2>\n<p>RNAPro is a state-of-the-art RNA 3D folding model developed through a synthesis of ideas from the previous Kaggle competition hosts and top-performing teams. It integrates several components that proved valuable in the previous competition and subsequent work: </p>\n<ul>\n<li>RibonanzaNet2 as an encoder for sequence and pairwise features</li>\n<li>RNA MSAs to provide rich input data beyond a single sequence</li>\n<li>3D structure templates </li>\n<li>RNA fine-tuned Protenix to take the above inputs and predict 3D structure</li>\n</ul>\n<p>This combination represents some of the best insights from the previous competition !</p>\n<p><em>⭐ <a href=\"https://github.com/NVIDIA-Digital-Bio/RNAPro\" target=\"_blank\">The code is available on GitHub</a> ⭐</em></p>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F2062758%2Ff9faa54f9c0a89e43aa7267cba97b23e%2Fimage%20(14).png?generation=1768576990418111&amp;alt=media\" alt=\"\"></p>\n<h2>What's included here?</h2>\n<p>We’re sharing two Kaggle notebooks that together create a complete inference and submission workflow: </p>\n<ul>\n<li><a href=\"https://www.kaggle.com/code/theoviel/stanford-rna-3d-folding-pt2-templates\" target=\"_blank\">Template preparation notebook</a></li>\n<li><a href=\"https://www.kaggle.com/code/theoviel/stanford-rna-3d-folding-pt2-rnapro-inference\" target=\"_blank\">Inference notebook</a></li>\n</ul>\n<p>These notebooks are intended to provide a strong baseline and starting point for the community, while being completely open. </p>\n<p>We are looking forward to seeing how the community builds on this baseline !</p>\n<h2>Further resources:</h2>\n<ul>\n<li><a href=\"https://www.biorxiv.org/content/10.64898/2025.12.30.696949v1.full.pdf\" target=\"_blank\">Preprint summarizing past competition insights including RNAPro</a></li>\n<li><a href=\"https://github.com/NVIDIA-Digital-Bio/RNAPro\" target=\"_blank\">RNAPro code</a></li>\n<li>Model weights: <a href=\"https://huggingface.co/nvidia/RNAPro-Private-Best-500M\" target=\"_blank\">Private-Best</a>, <a href=\"https://huggingface.co/nvidia/RNAPro-Public-Best-500M\" target=\"_blank\">Public-Best</a></li>\n<li><a href=\"https://www.kaggle.com/code/jaejohn/rnapro-inference-with-tbm\" target=\"_blank\">Offline inference code</a> by <a href=\"https://www.kaggle.com/jaejohn\" target=\"_blank\">@jaejohn</a></li>\n</ul>",
  "messages": [
    {
      "id": 3392269,
      "postDate": "2026-01-16T15:35:04.803Z",
      "content": "<p>We’re excited to share an inference workflow for RNAPro for part 2 of the Stanford RNA 3D Folding competition ! RNAPro reflects where the field landed after last year’s breakthroughs. </p>\n<h2>What is RNAPro?</h2>\n<p>RNAPro is a state-of-the-art RNA 3D folding model developed through a synthesis of ideas from the previous Kaggle competition hosts and top-performing teams. It integrates several components that proved valuable in the previous competition and subsequent work: </p>\n<ul>\n<li>RibonanzaNet2 as an encoder for sequence and pairwise features</li>\n<li>RNA MSAs to provide rich input data beyond a single sequence</li>\n<li>3D structure templates </li>\n<li>RNA fine-tuned Protenix to take the above inputs and predict 3D structure</li>\n</ul>\n<p>This combination represents some of the best insights from the previous competition !</p>\n<p><em>⭐ <a href=\"https://github.com/NVIDIA-Digital-Bio/RNAPro\" target=\"_blank\">The code is available on GitHub</a> ⭐</em></p>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F2062758%2Ff9faa54f9c0a89e43aa7267cba97b23e%2Fimage%20(14).png?generation=1768576990418111&amp;alt=media\" alt=\"\"></p>\n<h2>What's included here?</h2>\n<p>We’re sharing two Kaggle notebooks that together create a complete inference and submission workflow: </p>\n<ul>\n<li><a href=\"https://www.kaggle.com/code/theoviel/stanford-rna-3d-folding-pt2-templates\" target=\"_blank\">Template preparation notebook</a></li>\n<li><a href=\"https://www.kaggle.com/code/theoviel/stanford-rna-3d-folding-pt2-rnapro-inference\" target=\"_blank\">Inference notebook</a></li>\n</ul>\n<p>These notebooks are intended to provide a strong baseline and starting point for the community, while being completely open. </p>\n<p>We are looking forward to seeing how the community builds on this baseline !</p>\n<h2>Further resources:</h2>\n<ul>\n<li><a href=\"https://www.biorxiv.org/content/10.64898/2025.12.30.696949v1.full.pdf\" target=\"_blank\">Preprint summarizing past competition insights including RNAPro</a></li>\n<li><a href=\"https://github.com/NVIDIA-Digital-Bio/RNAPro\" target=\"_blank\">RNAPro code</a></li>\n<li>Model weights: <a href=\"https://huggingface.co/nvidia/RNAPro-Private-Best-500M\" target=\"_blank\">Private-Best</a>, <a href=\"https://huggingface.co/nvidia/RNAPro-Public-Best-500M\" target=\"_blank\">Public-Best</a></li>\n<li><a href=\"https://www.kaggle.com/code/jaejohn/rnapro-inference-with-tbm\" target=\"_blank\">Offline inference code</a> by <a href=\"https://www.kaggle.com/jaejohn\" target=\"_blank\">@jaejohn</a></li>\n</ul>",
      "rawMarkdown": "We’re excited to share an inference workflow for RNAPro for part 2 of the Stanford RNA 3D Folding competition ! RNAPro reflects where the field landed after last year’s breakthroughs. \n\n## What is RNAPro? \n\nRNAPro is a state-of-the-art RNA 3D folding model developed through a synthesis of ideas from the previous Kaggle competition hosts and top-performing teams. It integrates several components that proved valuable in the previous competition and subsequent work: \n- RibonanzaNet2 as an encoder for sequence and pairwise features\n- RNA MSAs to provide rich input data beyond a single sequence\n- 3D structure templates \n- RNA fine-tuned Protenix to take the above inputs and predict 3D structure\n\nThis combination represents some of the best insights from the previous competition !\n\n*⭐ [The code is available on GitHub](https://github.com/NVIDIA-Digital-Bio/RNAPro) ⭐*\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F2062758%2Ff9faa54f9c0a89e43aa7267cba97b23e%2Fimage%20(14).png?generation=1768576990418111&alt=media)\n\n## What's included here? \n\nWe’re sharing two Kaggle notebooks that together create a complete inference and submission workflow: \n- [Template preparation notebook](https://www.kaggle.com/code/theoviel/stanford-rna-3d-folding-pt2-templates)\n- [Inference notebook](https://www.kaggle.com/code/theoviel/stanford-rna-3d-folding-pt2-rnapro-inference)\n\nThese notebooks are intended to provide a strong baseline and starting point for the community, while being completely open. \n\nWe are looking forward to seeing how the community builds on this baseline !\n\n## Further resources: \n\n- [Preprint summarizing past competition insights including RNAPro](https://www.biorxiv.org/content/10.64898/2025.12.30.696949v1.full.pdf)\n- [RNAPro code](https://github.com/NVIDIA-Digital-Bio/RNAPro)\n- Model weights: [Private-Best](https://huggingface.co/nvidia/RNAPro-Private-Best-500M), [Public-Best](https://huggingface.co/nvidia/RNAPro-Public-Best-500M)\n- [Offline inference code](https://www.kaggle.com/code/jaejohn/rnapro-inference-with-tbm) by @jaejohn",
      "votes": 26
    },
    {
      "id": 3392306,
      "postDate": "2026-01-16T17:12:23.863Z",
      "content": "<p>My assumption is that over the next few days you will learn lots of ways your git hub install directions have issues :)</p>\n<p>I will start with mine - I absolutely hate with a strong passion code that downloads a large zip, extracts it and than deletes it.  In my case I have 4 local machines were I play with kaggle.  I would like to copy the zip to my NAS and than get things installed on all 4 machines.  Sadly - Did not notice the rm's until it was too late.  </p>\n<pre><code>cd release_data\nmkdir kaggle; cd kaggle\ncurl -L -o stanford-rna-3d-folding-all-atom-train-data.zip https://www.kaggle.com/api/v1/datasets/download/rhijudas/stanford-rna-3d-folding-all-atom-train-data\nunzip stanford-rna-3d-folding-all-atom-train-data.zip\nrm stanford-rna-3d-folding-all-atom-train-data.zip\n\n# Get MSAs from https://www.kaggle.com/datasets/rhijudas/clone-of-stanford-rna-3d-modeling-competition-data\ncurl -L -o stanford-rna-3d-folding.zip https://www.kaggle.com/api/v1/datasets/download/rhijudas/clone-of-stanford-rna-3d-modeling-competition-data\nmkdir MSA_v2\nunzip stanford-rna-3d-folding.zip \"MSA_v2/*\" -d /MSA_v2\nrm stanford-rna-3d-folding.zip\ncd ..\n</code></pre>",
      "rawMarkdown": "My assumption is that over the next few days you will learn lots of ways your git hub install directions have issues :)\n\nI will start with mine - I absolutely hate with a strong passion code that downloads a large zip, extracts it and than deletes it.  In my case I have 4 local machines were I play with kaggle.  I would like to copy the zip to my NAS and than get things installed on all 4 machines.  Sadly - Did not notice the rm's until it was too late.  \n\n```python\ncd release_data\nmkdir kaggle; cd kaggle\ncurl -L -o stanford-rna-3d-folding-all-atom-train-data.zip https://www.kaggle.com/api/v1/datasets/download/rhijudas/stanford-rna-3d-folding-all-atom-train-data\nunzip stanford-rna-3d-folding-all-atom-train-data.zip\nrm stanford-rna-3d-folding-all-atom-train-data.zip\n\n# Get MSAs from https://www.kaggle.com/datasets/rhijudas/clone-of-stanford-rna-3d-modeling-competition-data\ncurl -L -o stanford-rna-3d-folding.zip https://www.kaggle.com/api/v1/datasets/download/rhijudas/clone-of-stanford-rna-3d-modeling-competition-data\nmkdir MSA_v2\nunzip stanford-rna-3d-folding.zip \"MSA_v2/*\" -d /MSA_v2\nrm stanford-rna-3d-folding.zip\ncd ..\n```\n",
      "votes": 1,
      "replies": [
        {
          "id": 3392309,
          "postDate": "2026-01-16T17:14:24.390Z",
          "content": "<p>In line 9, you have: unzip stanford-rna-3d-folding.zip \"MSA_v2/*\" -d /MSA_v2</p>\n<p>The flag -d /MSA_v2 attempts to extract the files to the root of your file system (e.g., /MSA_v2), which will likely fail with a \"Permission denied\" error unless you are running as root.</p>\n<p>Since you created a local directory in line 8 (mkdir MSA_v2), you likely meant to extract to that local folder. You should remove the leading slash:</p>",
          "rawMarkdown": "In line 9, you have: unzip stanford-rna-3d-folding.zip \"MSA_v2/*\" -d /MSA_v2\n\nThe flag -d /MSA_v2 attempts to extract the files to the root of your file system (e.g., /MSA_v2), which will likely fail with a \"Permission denied\" error unless you are running as root.\n\nSince you created a local directory in line 8 (mkdir MSA_v2), you likely meant to extract to that local folder. You should remove the leading slash:",
          "votes": 1,
          "replies": [
            {
              "id": 3392319,
              "postDate": "2026-01-16T17:41:59.493Z",
              "content": "<p>I am guessing you changed folders and names at some point - but this does not work as no scripts folder seems to exist in anything I created so far.  But that py can be found in the RNAPro/preprocess/ folder.  </p>\n<p><code>python3 scripts/gen_ccd_cache.py</code></p>\n<p>This one takes some time - guessing that a few folks are hitting that server right now :)  Right now for me on a 1TB Xfinity system I had 1 hour and 10 minutes of download time.  Did the Xfinity speed test and my current download is 922 Mbps - so assuming the time is mostly the result of the server rather than my system.  </p>",
              "rawMarkdown": "I am guessing you changed folders and names at some point - but this does not work as no scripts folder seems to exist in anything I created so far.  But that py can be found in the RNAPro/preprocess/ folder.  \n\n`python3 scripts/gen_ccd_cache.py`\n\nThis one takes some time - guessing that a few folks are hitting that server right now :)  Right now for me on a 1TB Xfinity system I had 1 hour and 10 minutes of download time.  Did the Xfinity speed test and my current download is 922 Mbps - so assuming the time is mostly the result of the server rather than my system.  "
            },
            {
              "id": 3392320,
              "postDate": "2026-01-16T17:42:10.560Z",
              "content": "<blockquote>\n  <p>My assumption is that over the next few days you will learn lots of ways your git hub install directions have issues :)</p>\n</blockquote>\n<p>Yes, I already caught a few installing for inference on Kaggle, but I did not setup for training (yet?) so it can have issues.</p>\n<p>We'll fix them asap, thanks for the feedback :) </p>",
              "rawMarkdown": "> My assumption is that over the next few days you will learn lots of ways your git hub install directions have issues :)\n\nYes, I already caught a few installing for inference on Kaggle, but I did not setup for training (yet?) so it can have issues.\n\nWe'll fix them asap, thanks for the feedback :) "
            },
            {
              "id": 3392321,
              "postDate": "2026-01-16T17:43:43.590Z",
              "content": "<blockquote>\n  <p>python3 scripts/gen_ccd_cache.py</p>\n</blockquote>\n<p>This one is known, I have a MR that fixes it </p>",
              "rawMarkdown": "> python3 scripts/gen_ccd_cache.py\n\nThis one is known, I have a MR that fixes it "
            },
            {
              "id": 3392345,
              "postDate": "2026-01-16T18:33:19.933Z",
              "content": "<p>In case you did not guess :)   I am working on use for multiple local machines, so my issues might be a bit different than a kaggle install.</p>",
              "rawMarkdown": "In case you did not guess :)   I am working on use for multiple local machines, so my issues might be a bit different than a kaggle install."
            }
          ]
        }
      ]
    },
    {
      "id": 3419651,
      "postDate": "2026-03-11T09:28:33.200Z",
      "content": "<p>thanks for share</p>",
      "rawMarkdown": "thanks for share"
    }
  ],
  "comments": [
    {
      "id": 3392306,
      "author_name": "PC Jimmmy",
      "author_url": "",
      "post_date": "2026-01-16T17:12:23.863000",
      "content": "<p>My assumption is that over the next few days you will learn lots of ways your git hub install directions have issues :)</p>\n<p>I will start with mine - I absolutely hate with a strong passion code that downloads a large zip, extracts it and than deletes it.  In my case I have 4 local machines were I play with kaggle.  I would like to copy the zip to my NAS and than get things installed on all 4 machines.  Sadly - Did not notice the rm's until it was too late.  </p>\n<pre><code>cd release_data\nmkdir kaggle; cd kaggle\ncurl -L -o stanford-rna-3d-folding-all-atom-train-data.zip https://www.kaggle.com/api/v1/datasets/download/rhijudas/stanford-rna-3d-folding-all-atom-train-data\nunzip stanford-rna-3d-folding-all-atom-train-data.zip\nrm stanford-rna-3d-folding-all-atom-train-data.zip\n\n# Get MSAs from https://www.kaggle.com/datasets/rhijudas/clone-of-stanford-rna-3d-modeling-competition-data\ncurl -L -o stanford-rna-3d-folding.zip https://www.kaggle.com/api/v1/datasets/download/rhijudas/clone-of-stanford-rna-3d-modeling-competition-data\nmkdir MSA_v2\nunzip stanford-rna-3d-folding.zip \"MSA_v2/*\" -d /MSA_v2\nrm stanford-rna-3d-folding.zip\ncd ..\n</code></pre>",
      "votes": 1,
      "replies": [
        {
          "id": 3392309,
          "author_name": "PC Jimmmy",
          "author_url": "",
          "post_date": "2026-01-16T17:14:24.390000",
          "content": "<p>In line 9, you have: unzip stanford-rna-3d-folding.zip \"MSA_v2/*\" -d /MSA_v2</p>\n<p>The flag -d /MSA_v2 attempts to extract the files to the root of your file system (e.g., /MSA_v2), which will likely fail with a \"Permission denied\" error unless you are running as root.</p>\n<p>Since you created a local directory in line 8 (mkdir MSA_v2), you likely meant to extract to that local folder. You should remove the leading slash:</p>",
          "votes": 1,
          "replies": [
            {
              "id": 3392319,
              "author_name": "PC Jimmmy",
              "author_url": "",
              "post_date": "2026-01-16T17:41:59.493000",
              "content": "<p>I am guessing you changed folders and names at some point - but this does not work as no scripts folder seems to exist in anything I created so far.  But that py can be found in the RNAPro/preprocess/ folder.  </p>\n<p><code>python3 scripts/gen_ccd_cache.py</code></p>\n<p>This one takes some time - guessing that a few folks are hitting that server right now :)  Right now for me on a 1TB Xfinity system I had 1 hour and 10 minutes of download time.  Did the Xfinity speed test and my current download is 922 Mbps - so assuming the time is mostly the result of the server rather than my system.  </p>",
              "votes": 0,
              "replies": []
            },
            {
              "id": 3392320,
              "author_name": "Theo Viel",
              "author_url": "",
              "post_date": "2026-01-16T17:42:10.560000",
              "content": "<blockquote>\n  <p>My assumption is that over the next few days you will learn lots of ways your git hub install directions have issues :)</p>\n</blockquote>\n<p>Yes, I already caught a few installing for inference on Kaggle, but I did not setup for training (yet?) so it can have issues.</p>\n<p>We'll fix them asap, thanks for the feedback :) </p>",
              "votes": 0,
              "replies": []
            },
            {
              "id": 3392321,
              "author_name": "Theo Viel",
              "author_url": "",
              "post_date": "2026-01-16T17:43:43.590000",
              "content": "<blockquote>\n  <p>python3 scripts/gen_ccd_cache.py</p>\n</blockquote>\n<p>This one is known, I have a MR that fixes it </p>",
              "votes": 0,
              "replies": []
            },
            {
              "id": 3392345,
              "author_name": "PC Jimmmy",
              "author_url": "",
              "post_date": "2026-01-16T18:33:19.933000",
              "content": "<p>In case you did not guess :)   I am working on use for multiple local machines, so my issues might be a bit different than a kaggle install.</p>",
              "votes": 0,
              "replies": []
            }
          ]
        }
      ]
    },
    {
      "id": 3419651,
      "author_name": "Cat D",
      "author_url": "",
      "post_date": "2026-03-11T09:28:33.200000",
      "content": "<p>thanks for share</p>",
      "votes": 0,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3392269": "We’re excited to share an inference workflow for RNAPro for part 2 of the Stanford RNA 3D Folding competition ! RNAPro reflects where the field landed after last year’s breakthroughs. \n\n## What is RNAPro? \n\nRNAPro is a state-of-the-art RNA 3D folding model developed through a synthesis of ideas from the previous Kaggle competition hosts and top-performing teams. It integrates several components that proved valuable in the previous competition and subsequent work: \n- RibonanzaNet2 as an encoder for sequence and pairwise features\n- RNA MSAs to provide rich input data beyond a single sequence\n- 3D structure templates \n- RNA fine-tuned Protenix to take the above inputs and predict 3D structure\n\nThis combination represents some of the best insights from the previous competition !\n\n*⭐ [The code is available on GitHub](https://github.com/NVIDIA-Digital-Bio/RNAPro) ⭐*\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F2062758%2Ff9faa54f9c0a89e43aa7267cba97b23e%2Fimage%20(14).png?generation=1768576990418111&alt=media)\n\n## What's included here? \n\nWe’re sharing two Kaggle notebooks that together create a complete inference and submission workflow: \n- [Template preparation notebook](https://www.kaggle.com/code/theoviel/stanford-rna-3d-folding-pt2-templates)\n- [Inference notebook](https://www.kaggle.com/code/theoviel/stanford-rna-3d-folding-pt2-rnapro-inference)\n\nThese notebooks are intended to provide a strong baseline and starting point for the community, while being completely open. \n\nWe are looking forward to seeing how the community builds on this baseline !\n\n## Further resources: \n\n- [Preprint summarizing past competition insights including RNAPro](https://www.biorxiv.org/content/10.64898/2025.12.30.696949v1.full.pdf)\n- [RNAPro code](https://github.com/NVIDIA-Digital-Bio/RNAPro)\n- Model weights: [Private-Best](https://huggingface.co/nvidia/RNAPro-Private-Best-500M), [Public-Best](https://huggingface.co/nvidia/RNAPro-Public-Best-500M)\n- [Offline inference code](https://www.kaggle.com/code/jaejohn/rnapro-inference-with-tbm) by @jaejohn",
    "3392306": "My assumption is that over the next few days you will learn lots of ways your git hub install directions have issues :)\n\nI will start with mine - I absolutely hate with a strong passion code that downloads a large zip, extracts it and than deletes it.  In my case I have 4 local machines were I play with kaggle.  I would like to copy the zip to my NAS and than get things installed on all 4 machines.  Sadly - Did not notice the rm's until it was too late.  \n\n```python\ncd release_data\nmkdir kaggle; cd kaggle\ncurl -L -o stanford-rna-3d-folding-all-atom-train-data.zip https://www.kaggle.com/api/v1/datasets/download/rhijudas/stanford-rna-3d-folding-all-atom-train-data\nunzip stanford-rna-3d-folding-all-atom-train-data.zip\nrm stanford-rna-3d-folding-all-atom-train-data.zip\n\n# Get MSAs from https://www.kaggle.com/datasets/rhijudas/clone-of-stanford-rna-3d-modeling-competition-data\ncurl -L -o stanford-rna-3d-folding.zip https://www.kaggle.com/api/v1/datasets/download/rhijudas/clone-of-stanford-rna-3d-modeling-competition-data\nmkdir MSA_v2\nunzip stanford-rna-3d-folding.zip \"MSA_v2/*\" -d /MSA_v2\nrm stanford-rna-3d-folding.zip\ncd ..\n```\n",
    "3419651": "thanks for share"
  }
}