license: cc-by-4.0
tags:
- biology
- chemistry
- medical
- immunology
- immunogenicity
pretty_name: ImmunoStruct
size_categories:
- 10K<n<100K
ImmunoStruct
ImmunoStruct enables multimodal deep learning for immunogenicity prediction
Project leads: Kevin Bijan Givechian, João Felipe Rocha, Chen Liu.
Correspondence: akiko.iwasaki@yale.edu, smita.krishnaswamy@yale.edu.
Instructions on preparing everything and running training/inference is provided on our official GitHub repository.
The pre-trained model weights for IEDB and CEDAR datasets are available at our huggingface model repo.
In short, the data can be downloaded using
hf download ChenLiu1996/ImmunoStruct --repo-type dataset --local-dir ./
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.
- 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 - AlphaFold2 structures, in PyTorch Geometric format.
graph_pyg_IEDB.zip graph_pyg_CEDAR_cancer.zip graph_pyg_CEDAR_wildtype.zip graph_pyg_clinical.zip - (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