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.
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
Sample Usage
You can use the pepbenchmark library to load and manage the datasets:
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
@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}
}