---
task_categories:
- text-classification
tags:
- biology
- peptide
- drug-discovery
---

This dataset is part of **PepBenchmark**, a standardized benchmark for peptide machine learning introduced in the paper [PepBenchmark: A Standardized Benchmark for Peptide Machine Learning](https://huggingface.co/papers/2604.10531).

PepBenchmark unifies datasets, preprocessing, and evaluation protocols for peptide drug discovery. It comprises three components:
- **PepBenchData**: A well-curated collection of 29 canonical-peptide and 6 non-canonical-peptide datasets across 7 groups.
- **PepBenchPipeline**: A standardized preprocessing pipeline ensuring consistent data cleaning, construction, and splitting.
- **PepBenchLeaderboard**: A unified evaluation protocol and leaderboard with strong baselines across major methodological families.

- **GitHub Repository**: [https://github.com/ZGCI-AI4S-Pep/PepBenchmark](https://github.com/ZGCI-AI4S-Pep/PepBenchmark)
- **Paper**: [https://huggingface.co/papers/2604.10531](https://huggingface.co/papers/2604.10531)

## Sample Usage

You can use the `pepbenchmark` library to load and manage the datasets:

```python
from pepbenchmark.dataset_manager.single_dataset import SinglePeptideDatasetManager

# Initialize the manager for a specific dataset
manager = SinglePeptideDatasetManager(
    "ace_inhibitory",
    official_feature_names=["fasta", "label"],
    dataset_dir="../PepBenchData/PepBenchData-50",
)

# Access features
sequences = manager.get_feature("fasta")
labels = manager.get_feature("label")

# Set data splits
splits = manager.set_official_split_indices(
    split_type="hybrid_split",
    fold_seed=0
)

print(f"Train samples: {len(splits['train'])}")
print(f"Validation samples: {len(splits['valid'])}")
print(f"Test samples: {len(splits['test'])}")
```

## Citation

```bibtex
@inproceedings{zhang2026pepbenchmark,
  title={PepBenchmark: A Standardized Benchmark for Peptide Machine Learning},
  author={Zhang, Jiahui and Wang, Rouyi and Zhou, Kuangqi and Xiao, Tianshu and Zhu, Lingyan and Min, Yaosen and Wang, Yang},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026},
  url={https://openreview.net/forum?id=NskQgtSdll}
}
```