---
license: cc-by-4.0
tags:
- biology
- chemistry
- medical
- immunology
- immunogenicity
pretty_name: ImmunoStruct
size_categories:
- 10K<n<100K
---

<img src="https://raw.githubusercontent.com/KrishnaswamyLab/ImmunoStruct/main/assets/ImmunoStruct_cover.png" width="200">

# ImmunoStruct
[ImmunoStruct enables multimodal deep learning for immunogenicity prediction](https://www.nature.com/articles/s42256-025-01163-y)

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Project leads: [Kevin Bijan Givechian](https://www.linkedin.com/in/kevin-bijan-givechian-phd-36467ba3/), [João Felipe Rocha](https://www.linkedin.com/in/joao-felipe-rocha/), [Chen Liu](https://www.linkedin.com/in/chenliu1996/).
<br>Correspondence: `akiko.iwasaki@yale.edu`, `smita.krishnaswamy@yale.edu`.

<img src="https://raw.githubusercontent.com/KrishnaswamyLab/ImmunoStruct/main/assets/schematic.png" alt="ImmunoStruct Architecture" width="800">

Instructions on preparing everything and running training/inference is provided on [our official GitHub repository](https://github.com/KrishnaswamyLab/ImmunoStruct).

The pre-trained model weights for IEDB and CEDAR datasets are available at [our huggingface model repo](https://huggingface.co/ChenLiu1996/ImmunoStruct).

In short, the data can be downloaded using
<br>`hf download ChenLiu1996/ImmunoStruct --repo-type dataset --local-dir ./`

**NOTE: [Our official repository](https://github.com/KrishnaswamyLab/ImmunoStruct) also includes detailed instructions and working code on how to run protein folding using AlphaFold2.**

## 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.

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 find ImmunoStruct helpful to your research, please cite our paper:

BibTeX:
```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:<br>
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