| --- |
| 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} |
| } |
| ``` |
