PepBenchData_raw

This repository provides the raw dataset collected for PepBenchmark: A Standardized Benchmark for Peptide Machine Learning.

Paper | GitHub | Processed Dataset (PepBenchData)

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Difference Between PepBenchData_raw and PepBenchData

We provide two related but distinct datasets:

1. PepBenchData_raw (This Repository)

• Contains raw peptide sequences collected from various databases and published literature.
• Most samples are experimentally validated, ensuring high reliability.
• No extensive preprocessing or feature engineering is applied.
• Recommended for:
  • Generative modeling
  • Pretraining tasks
  • Studies requiring high-confidence biological sequences

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2. PepBenchData

• A processed dataset designed for machine learning tasks.
• Includes:
  • Feature representations
  • Standardized train/validation/test splits
• Construction involves several preprocessing steps:
  • Data filtering
  • Redundancy removal among positive samples (e.g., via MMseqs2)
  • Negative sample construction and sampling

Important Note:
During negative sample construction, some sequences are sampled from UniRef50.
As a result, not all negative samples are experimentally validated.

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Sample Usage

You can use the pepbenchmark library to manage and load these datasets. Below is an example of how to load a dataset and its official splits:

from pepbenchmark.dataset_manager.single_dataset import SinglePeptideDatasetManager

# Initialize the manager for a specific task (e.g., ACE inhibitory)
manager = SinglePeptideDatasetManager(
    "ace_inhibitory",
    official_feature_names=["fasta", "label"],
    dataset_dir="path/to/PepBenchData",
)

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

# Load official splits
splits = manager.set_official_split_indices(
    split_type="hybrid_split",
    fold_seed=0
)

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

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Data Sources

The raw dataset is aggregated from multiple sources, including public biological databases and published scientific literature.

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Dataset Statistics

Classification Tasks

• pos.csv: Experimentally validated positive samples
• neg.csv: Experimentally validated negative samples (Note: For many datasets, neg.csv is not available.)
• pos_filter_id_0.9_cov_f0.9.csv: Positive samples after redundancy removal using MMseqs2 (identity ≥ 0.9, coverage ≥ 0.9).

Regression Tasks

Contains datasets with continuous labels for peptide-related properties.

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Directory Structure (Classification Datasets)

dataset_name/
├── pos.csv
├── neg.csv (optional)
└── pos_filter_id_0.9_cov_f0.9.csv

Citation

If you use PepBenchmark, please cite:

@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}
}