{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":118765,"databundleVersionId":15231210,"sourceType":"competition"}],"dockerImageVersionId":31259,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-01-22T15:04:06.679551Z","iopub.execute_input":"2026-01-22T15:04:06.679957Z","iopub.status.idle":"2026-01-22T15:05:10.023902Z","shell.execute_reply.started":"2026-01-22T15:04:06.679921Z","shell.execute_reply":"2026-01-22T15:05:10.022639Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n# 🧬 Stanford RNA 3D Folding  \n\n\n# Step 0: Ignore warnings\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# Step 1: Install required packages (if running fresh kernel)\n!pip install --quiet rnapy biopython tqdm\n\n# Step 2: Imports\nimport pandas as pd\nfrom Bio import SeqIO\nfrom rnapy import RhoFold\nfrom tqdm import tqdm\n\n# Step 3: Load Test Sequences\ntest_file = \"/kaggle/input/stanford-rna-3d-folding-2/test.fasta\"\ntest_sequences = list(SeqIO.parse(test_file, \"fasta\"))\nprint(f\"✅ Loaded {len(test_sequences)} sequences\")\n\n# Step 4: Predict 3D structures\npredictions = {}\nfor record in tqdm(test_sequences, desc=\"Predicting structures\"):\n    seq_id = record.id\n    seq = str(record.seq)\n    \n    # Generate 3D prediction\n    # Note: RhoFold may produce multiple predictions per sequence if needed\n    predicted_structure = RhoFold.predict_3d(seq)  # returns PDB string or coordinates\n    \n    predictions[seq_id] = predicted_structure\n\n# Step 5: Prepare submission\nsubmission = pd.DataFrame({\n    \"Id\": list(predictions.keys()),\n    \"Predicted\": list(predictions.values())  # Make sure format matches competition requirements\n})\nsubmission_file = \"submission.csv\"\nsubmission.to_csv(submission_file, index=False)\nprint(f\"✅ Submission saved: {submission_file}\")\nsubmission.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-01-27T16:33:53.731672Z","iopub.execute_input":"2026-01-27T16:33:53.73216Z"}},"outputs":[],"execution_count":null}]}