ImmunoStruct

ImmunoStruct enables multimodal deep learning for immunogenicity prediction

Dataset details

In this huggingface dataset, we include all data used in the paper.

• Necessary for running training and/or inference on IEDB: 1, 2.
• Necessary for running training and/or inference on CEDAR: 1, 2.
• Necessary for running inference on clinical validation data: 1, 2.
• Necessary if you want to build your graph differently: 3.

Check out how to use it on our official GitHub repository: https://github.com/KrishnaswamyLab/ImmunoStruct.

1. CSV files of (protein sequences, biochemical property values, and immunogenicity scores) for all 3 datasets (IEDB, CEDAR, and clinical validation), CSV file of clinical survival data, and CSV file of MHC (a.k.a. HLA) sequences.

   ImmunoStruct_IEDB_data.csv
   ImmunoStruct_CEDAR_data_cancer.csv
   ImmunoStruct_CEDAR_data_wildtype.csv
   ImmunoStruct_clinical_data.csv
   ImmunoStruct_clinical_data_survival.csv
   HLA_allele_sequences.csv
   

2. AlphaFold2 structures, in PyTorch Geometric format.

   graph_pyg_IEDB.zip
   graph_pyg_CEDAR_cancer.zip
   graph_pyg_CEDAR_wildtype.zip
   graph_pyg_clinical.zip
   

3. (Optional) AlphaFold2 structures, in raw PDB format.

   alphafold2_pdb_IEDB.zip
   alphafold2_pdb_CEDAR_cancer.zip
   alphafold2_pdb_CEDAR_wildtype.zip
   alphafold2_pdb_clinical.zip
   

Citation

If you use ImmunoStruct in your research, please cite our paper:

BibTeX:

@article{givechian2026immunostruct,
  title={ImmunoStruct enables multimodal deep learning for immunogenicity prediction},
  author={Givechian, Kevin Bijan and Rocha, Jo{\~a}o Felipe and Liu, Chen and Yang, Edward and Tyagi, Sidharth and Greene, Kerrie and Ying, Rex and Caron, Etienne and Iwasaki, Akiko and Krishnaswamy, Smita},
  journal={Nature Machine Intelligence},
  volume={8},
  pages={70--83},
  year={2026},
  publisher={Nature Publishing Group UK London}
}

Nature format:
Givechian, K.B., Rocha, J.F., Liu, C. et al. ImmunoStruct enables multimodal deep learning for immunogenicity prediction. Nat Mach Intell 8, 70–83 (2026). https://doi.org/10.1038/s42256-025-01163-y