HakimT/peptideforge-dataset

PeptideForge Dataset

Dataset Description

This dataset repo packages the processed training, validation, and test splits used by the PeptideForge project for conditioned peptide generation and AMP scoring.

It exposes three Hub configs:

config	purpose	splits
generator_text	Conditioned text corpus exported as parsed CSV rows	train / validation / test
generator_structured	Structured generator table with features and conditioned prompts	train / validation / test
scorer	Structured AMP/non-AMP scorer dataset	train / validation / test

Dataset Summary

• Total rows across all published configs: 221424
• Generator text corpus: prompt-style conditioned peptide records derived from the line-based train_core.csv / val_core.csv / test_core.csv files
• Generator structured tables: per-sequence features plus conditioned_text
• Scorer tables: labeled AMP vs non-AMP examples with the features used by the AMP scorer

Splits

config	train	validation	test
generator_text	23791	2974	2974
generator_structured	23791	2974	2974
scorer	129556	16195	16195

Feature Schemas

generator_text

Columns: conditioned_text, sequence, length_tag, charge_tag, hydro_tag

Each example stores the raw conditioned line plus the parsed sequence and the three conditioning tags.

generator_structured

Columns: id, sequence, label, length, unique_chars, is_standard, charge, hydrophobicity, frac_basic, frac_acidic, cysteine_count, length_bin, charge_bin, hydro_bin, condition_prefix, conditioned_text

These rows are the structured generator tables used for analysis and for workflows that want explicit feature columns in addition to the prompt-style conditioning text.

scorer

Columns: id, sequence, label, length, unique_chars, is_standard, charge, hydrophobicity, frac_basic, frac_acidic, cysteine_count, length_bin, charge_bin, hydro_bin, condition_prefix, conditioned_text

These rows contain AMP/non-AMP labels and the feature set used by scorer/scorer.py.

Source Data Layout

The original code repository stores the processed files in:

• data/generator/
• data/scorer/
• data/other/

The original repo data/ tree is mirrored under source_data/ in this dataset repo so the exact CSV/text files used by the codebase stay inspectable.

Loading The Data

from datasets import load_dataset

generator_text = load_dataset("HakimT/peptideforge-dataset", "generator_text")
generator_structured = load_dataset("HakimT/peptideforge-dataset", "generator_structured")
scorer = load_dataset("HakimT/peptideforge-dataset", "scorer")

Load a single split directly:

train_generator_text = load_dataset("HakimT/peptideforge-dataset", "generator_text", split="train")

Data Provenance

The processed data in this project is derived from:

Peng, Shuang; Rajjou, Loïc, 2024, "Unifying Antimicrobial Peptide Datasets for Robust Deep Learning-Based Classification", Recherche Data Gouv, V1, https://doi.org/10.57745/NZ0IRX

This Hugging Face dataset repo contains processed and reformatted derivatives used by the PeptideForge training, evaluation, and scoring pipelines.

Preprocessing Notes

• Generator text examples encode peptide conditioning tags inline and are exported here as a parsed CSV for easier loading on the Hub.
• Structured generator tables retain the same conditioning information in explicit feature columns, including condition_prefix and conditioned_text.
• The scorer split preserves AMP labels and physicochemical features for classifier training and offline evaluation.

Intended Uses

• Reproducing the training and evaluation flows in the PeptideForge codebase
• Training conditioned peptide generators from prompt-style or tabular representations
• Training or benchmarking AMP scoring/classification pipelines

Limitations

• This is a processed research dataset, not a clinical decision-making resource.
• The conditioning tags are coarse bins, not precise biophysical targets.
• Generated peptides still require downstream validation.

Licensing

Unless otherwise noted, the processed data files distributed from the PeptideForge project are intended to be shared under CC BY 4.0, consistent with the repository's top-level license notice.

Citation

If you use this dataset, cite both the upstream AMP dataset source and the PeptideForge repository that produced these processed splits.