Category	Description	Files	Size
End-to-end evaluation	Cascade vs Flat (5+5 runs × 100K), GPT vs BERT infilling (3 ratios × 5+5 runs), constrained generation (9 scenarios)	219	1.4 GB
Per-stage evaluation	Layout (3 archs), Content (4 archs × 3 sizes), Connection (5 archs × 3 sizes)	75	92 MB
Prediction evaluation	LLM + GNN (4 archs × 3 sizes × 2 encodings), ensemble comparison	97	1.2 MB
Candidate analysis	Screening results from 4.78M novel generated molecules	4	19 MB

Total size: ~1.5 GB

Data Format

Each experiment directory contains a subset of:

File	Format	Description
`eval_report.json`	JSON	Pre-computed evaluation metrics (validity, uniqueness, novelty, internal diversity, NN distance, per-type statistics). Primary data source for visualization.
`raw.jsonl`	JSONL	Per-molecule generation results (HELM, SMILES, connection types, roundtrip validation).
`candidates.csv`	CSV	Valid molecules with SMILES (subset of `raw.jsonl`).
`eval_summary.txt`	Text	Human-readable evaluation summary.
`eval_test.json`	JSON	Per-stage model test metrics (loss, accuracy, confusion matrix).
`inference.json`	JSON	Per-stage inference outputs for distribution analysis.
`metrics.json`	JSON	Per-stage training metrics (per-epoch loss / accuracy curves).

File Structure

pepforge-fig-data/
└── Data/
    ├── Generation_Model/
    │   ├── Layout/{GPT,GRU,LSTM}/                 # 3 archs × eval_test + inference + metrics
    │   ├── Content/{GPT,GRU,LSTM,BERT}/{SMALL,MEDIUM,LARGE}/
    │   ├── Connection/{GAT,GCN,GIN,MPNN,GRAPH_TRANSFORMER}/{SMALL,MEDIUM,LARGE}/
    │   └── End2end/
    │       ├── Cascade_vs_Flat/                   # Main comparison (Fig 3 / S5)
    │       │   ├── Cascade/run{1..5}/             # eval_report.json + raw.jsonl + ...
    │       │   └── Flat/run{1..5}/
    │       ├── GPT_vs_BERT_Infilling/             # Infilling comparison (Fig 3 / S6)
    │       │   ├── constraints/                   # Pre-sampled constraint files
    │       │   └── ratio_{0.3,0.5,0.7}/{GPT,BERT}/run{1..5}/
    │       └── Constrained_Generation/{CG1_L,CG2_LC_Cys,CG3_LCC_SS,CG4_LCC_Lanthi,
    │                                  CG5_L_multi,CG6_LCC_multi_Sulfa,CG7_L_filter_SS,
    │                                  CG8_LayoutRange,CG9_PEPonly}/
    ├── Prediction_Model/
    │   ├── HELM/{LLM,GNN}/{arch}/{SMALL,MEDIUM,LARGE}/    # 48 evals
    │   ├── SMILES/{LLM,GNN}/{arch}/{SMALL,MEDIUM,LARGE}/  # 48 evals
    │   └── Inference/ensemble_comparison.json
    └── Candidate_Analysis/
        ├── eval_report.json                       # Screening evaluation (NN distance, FP stats)
        ├── AMP_active.csv                         # AMP-active candidates (39,891 rows, class 3/4)
        └── AMP_hit.csv                            # Hit set: Active ∧ non_hem ∧ non_tox (799 rows)

The Candidate_Analysis split reflects the rescored library after retraining the AMP ensemble on MIC-only DBAASP data: 4.78M novel → 39,891 active → 799 hit (triple filter). The legacy AMP_active_druglike.csv from the previous release has been replaced by AMP_hit.csv (same column schema, current numbers).

Corresponding Figures

Figure	Data Source	Notebook
Fig 3a–f	`Generation_Model/End2end/Cascade_vs_Flat/{Cascade,Flat}/run{1..5}/eval_report.json`	`Generation_Model/End2end/Cascade_vs_Flat/flat_cascade_multirun.ipynb`
Fig 3g–i	`Generation_Model/End2end/GPT_vs_BERT_Infilling/ratio_*/eval_report.json`	`Generation_Model/End2end/GPT_vs_BERT_Infilling/Infilling_Generation.ipynb`
Fig 4a–b	`Prediction_Model/{HELM,SMILES}/{LLM,GNN}///eval_test.json` + `Prediction_Model/Inference/ensemble_comparison.json`	`Prediction_Model/prediction.ipynb`
Fig 4c–e	`Candidate_Analysis/eval_report.json` + `Candidate_Analysis/AMP_active.csv` + `AMP_hit.csv`	`Candidate_Analysis/candidate_analysis.ipynb`
SI: Layout / Content / Connection	`Generation_Model/{Layout,Content,Connection}/...`	per-stage notebooks
SI: Constrained generation	`Generation_Model/End2end/Constrained_Generation/CG*/`	`constrained_generation.ipynb`

Quick Start

# Clone PepForge and download figure data
git clone https://github.com/wqx1999/PepForge.git
cd PepForge
python install.py --download paper    # Downloads this dataset
python install.py --download data     # Downloads training data (some notebooks need it)

# Run a visualization notebook
cd Paper/Figures/Generation_Model/End2end/Cascade_vs_Flat/
jupyter notebook flat_cascade_multirun.ipynb

Related Resources

Code + Notebooks: wqx1999/PepForge
Models: pepforge-model
Training data: pepforge-training-data
Generated library: pepforge-generated-data

Citation

@article{wang2026pepforge,
  title={PepForge: Hierarchical HELM-Based Peptide Generation},
  author={Wang, Qingxin and Süssmuth, Roderich D.},
  journal={bioRxiv},
  year={2026},
  doi={10.64898/2026.05.29.728379},
  url={https://www.biorxiv.org/content/10.64898/2026.05.29.728379v1}
}

License

CC-BY-4.0

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