Datasets:

RosettaCommons
/

SAAINTDB

license: cc-by-4.0
language: en
tags:
  - biology
  - antibody
  - protein-structure
  - mmcif
  - pdb
  - rosettacommons
pretty_name: Antibody dataset
repo: https://github.com/tommyhuangthu/SAAINT
dataset_summary: >-
  This dataset is a curated and processed version of the antibody dataset
  originally introduced in the SAAINT-DB paper. We converted the original
  dataset into a structured dataset compatible with the Hugging Face Datasets.
  The dataset contains 21,400 antibody entries derived from 11,304 PDB
  structures.
Citation_bibtex: |-
  @article{Huang2025,
    title = {SAAINT-DB: a comprehensive structural antibody database for antibody modeling and design},
    volume = {46},
    ISSN = {1745-7254},
    url = {http://dx.doi.org/10.1038/s41401-025-01608-5},
    DOI = {10.1038/s41401-025-01608-5},
    number = {12},
    journal = {Acta Pharmacologica Sinica},
    publisher = {Springer Science and Business Media LLC},
    author = {Huang,  Xiaoqiang and Zhou,  Jun and Chen,  Shuang and Xia,  Xiaofeng and Chen,  Y. Eugene and Xu,  Jie},
    year = {2025},
    month = jun,
    pages = {3365–3375}
  }
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: validation
        path: data/validation.csv
      - split: test
        path: data/test.csv

SAAINTDB

This dataset is a curated version of the SAAINT-DB converted into a format compatible with the Hugging Face Datasets for machine learning applications.

The dataset contains 21,400 antibody entries derived from 11,304 PDB structures, reflecting the available structures as of February 2026. Each entry corresponds to an antibody chain and is uniquely identified using the PDB_ID_chain field (PDB ID + chain ID).

Dataset Splits

The dataset was split at the PDB level into train, validation, and test sets (70/15/15). To maintain balanced distributions, the split was stratified based on the HL label (heavy/light chain availability). To prevent data leakage, all entries originating from the same PDB ID were assigned to the same split. Note: There are some PDBs with multiple antibodies, so the number of PDB files are fewer than the number of data entries.

The resulting splits are provided as CSV files in the data/ directory. The corresponding PDB structures for each split are also provided in the PDB/ directory.

train.csv rows: 15033 | number of unique PDB_ID in train split: 7649
validation.csv rows: 3179 | number of unique PDB_ID in validation split: 1639
test.csv rows: 3188 | number of unique PDB_ID in test split: 1639

Dataset Processing

Processing scripts are provided in the src/ directory.

The following preprocessing steps were performed to construct the dataset:

Added a PDB_ID_chain column to uniquely identify each antibody entry by concatenating the PDB ID with the corresponding antibody chain identifier. This ensures that multiple antibody chains originating from the same PDB structure can be distinguished and treated as separate entries
Added an hl_label column indicating chain availability:

HL: both heavy and light chains present
H_only: only heavy chain present
L_only: only light chain present
This label was later used for balanced dataset splitting.

Some PDB entries referenced in the dataset were missing structure files.
We identified the missing entries and downloaded 111 mmCIF files from the RCSB Protein Data Bank (PDB), updating the dataset to reflect the available structures as of February 2026. This might happened becuase the up-to-date SAAINT-DB dataset was generated in February 2026, while the PDB files were uploaded in January 2026.
FASTA files corresponding to the downloaded CIF structures were missing and were subsequently generated/added.
The dataset was split into train, validation, and test sets (70/15/15).
A split column was added to the dataset to indicate the assigned subset (train, validation, or test).
The processed dataset is organized as follows:

data/ : contains three CSV files (train.csv, validation.csv, test.csv)
PDB/ : contains PDB structure files organized into train/, validation/, and test/ directories

For reproducibility, the file saaintdb_raw_data_20260226.csv corresponds to the original dataset file saaintdb_20260226_all.tsv, which was downloaded from the official SAAINTDB GitHub repository and converted to CSV format for easier processing.

Quickstart Usage

Install HuggingFace Datasets package

Each subset can be loaded into python using the Huggingface datasets library. First, from the command line install the datasets library

$ pip install datasets

then, from within python load the datasets library

>>> import datasets

Load Dataset

Load SAAINTDB dataset.

>>> SAAINTDB = datasets.load_dataset('RosettaCommons/SAAINTDB')
README.md: 4.01kB [00:00, 18.8MB/s]
train.csv: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 18.0M/18.0M [00:00<00:00, 64.8MB/s]
validation.csv: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 3.82M/3.82M [00:00<00:00, 34.6MB/s]
test.csv: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 3.82M/3.82M [00:00<00:00, 58.8MB/s]
Generating train split: 100%|██████████████████████████████████████████████████████████████████████████| 15033/15033 [00:00<00:00, 42972.11 examples/s]
Generating validation split: 100%|███████████████████████████████████████████████████████████████████████| 3179/3179 [00:00<00:00, 43227.91 examples/s]
Generating test split: 100%|█████████████████████████████████████████████████████████████████████████████| 3188/3188 [00:00<00:00, 46207.52 examples/s]

and the dataset is loaded as a datasets.arrow_dataset.Dataset

>>> SAAINTDB
DatasetDict({
    train: Dataset({
        features: ['PDB_ID_chain', 'PDB_ID', 'Title', 'Mutation(s)', 'Classification', 'Deposit_date', 'Release_date', 'Method', 'Resolution', 'R_free', 'R_work', 'PMID', 'DOI', 'Model_index', 'Asym_ID_type', 'Ab_type', 'H_subgroup', 'L_subgroup', 'H_chain_ID', 'L_chain_ID', 'H_fas_seq', 'L_fas_seq', 'H_filled_pdb_seq', 'L_filled_pdb_seq', 'H_mean_radius', 'L_mean_radius', 'H_fas_seq_len', 'L_fas_seq_len', 'H_pdb_seq_len', 'L_pdb_seq_len', 'H_filled_seq_len', 'L_filled_seq_len', 'HL_inf_res_num', 'H_mol_name', 'L_mol_name', 'H_species', 'L_species', 'Ag_chain_ID(s)', 'Ag_type(s)', 'Ag_mol_name(s)', 'Ag_species', 'Ab_ag_inf_res_num', 'CDR_inf_res_num', 'CDR_inf_res_ratio', 'hl_label', 'split'],
        num_rows: 15033
    })
    validation: Dataset({
        features: ['PDB_ID_chain', 'PDB_ID', 'Title', 'Mutation(s)', 'Classification', 'Deposit_date', 'Release_date', 'Method', 'Resolution', 'R_free', 'R_work', 'PMID', 'DOI', 'Model_index', 'Asym_ID_type', 'Ab_type', 'H_subgroup', 'L_subgroup', 'H_chain_ID', 'L_chain_ID', 'H_fas_seq', 'L_fas_seq', 'H_filled_pdb_seq', 'L_filled_pdb_seq', 'H_mean_radius', 'L_mean_radius', 'H_fas_seq_len', 'L_fas_seq_len', 'H_pdb_seq_len', 'L_pdb_seq_len', 'H_filled_seq_len', 'L_filled_seq_len', 'HL_inf_res_num', 'H_mol_name', 'L_mol_name', 'H_species', 'L_species', 'Ag_chain_ID(s)', 'Ag_type(s)', 'Ag_mol_name(s)', 'Ag_species', 'Ab_ag_inf_res_num', 'CDR_inf_res_num', 'CDR_inf_res_ratio', 'hl_label', 'split'],
        num_rows: 3179
    })
    test: Dataset({
        features: ['PDB_ID_chain', 'PDB_ID', 'Title', 'Mutation(s)', 'Classification', 'Deposit_date', 'Release_date', 'Method', 'Resolution', 'R_free', 'R_work', 'PMID', 'DOI', 'Model_index', 'Asym_ID_type', 'Ab_type', 'H_subgroup', 'L_subgroup', 'H_chain_ID', 'L_chain_ID', 'H_fas_seq', 'L_fas_seq', 'H_filled_pdb_seq', 'L_filled_pdb_seq', 'H_mean_radius', 'L_mean_radius', 'H_fas_seq_len', 'L_fas_seq_len', 'H_pdb_seq_len', 'L_pdb_seq_len', 'H_filled_seq_len', 'L_filled_seq_len', 'HL_inf_res_num', 'H_mol_name', 'L_mol_name', 'H_species', 'L_species', 'Ag_chain_ID(s)', 'Ag_type(s)', 'Ag_mol_name(s)', 'Ag_species', 'Ab_ag_inf_res_num', 'CDR_inf_res_num', 'CDR_inf_res_ratio', 'hl_label', 'split'],
        num_rows: 3188
    })
})

which is a column oriented format that can be accessed directly, converted in to a pandas.DataFrame, or parquet format, e.g.

>>> SAAINTDB.data.column('pdb')
>>> SAAINTDB.to_pandas()
>>> SAAINTDB.to_parquet("dataset.parquet")

Uses

This dataset is intended for training and evaluating machine learning models on antibody structural data, particularly for tasks involving antibody chain characterization and antibody–antigen interaction analysis.

Because the dataset provides paired structural files (PDB), sequences (FASTA), and tabular metadata, it is suitable for workflows that integrate structural bioinformatics with machine learning.

Note: Be aware that when downloading the CSV files and opening them in Google Sheets or Microsoft Excel, the PDB_ID column may be automatically converted to scientific notation (e.g., 6e10 may appear as 6.00E10).

Dataset Card Author

Haneul Park (hahaneul@umich.edu)

Citation

  title = {SAAINT-DB: a comprehensive structural antibody database for antibody modeling and design},
  volume = {46},
  ISSN = {1745-7254},
  url = {http://dx.doi.org/10.1038/s41401-025-01608-5},
  DOI = {10.1038/s41401-025-01608-5},
  number = {12},
  journal = {Acta Pharmacologica Sinica},
  publisher = {Springer Science and Business Media LLC},
  author = {Huang,  Xiaoqiang and Zhou,  Jun and Chen,  Shuang and Xia,  Xiaofeng and Chen,  Y. Eugene and Xu,  Jie},
  year = {2025},
  month = jun,
  pages = {3365–3375}
}