AbDes: Antibody Description Dataset

Dataset Description

AbDes (Antibody Description Dataset) is a comprehensive multi-modal dataset designed for text-based antigen-conditioned antibody redesign. It unifies antibody-antigen structural information with rich textual descriptions, enabling novel AI-driven approaches to therapeutic antibody discovery.

Key Features

• 7,684 complete antibody-antigen pairs with heavy chain, light chain, and antigen sequences
• Rich textual descriptions derived from PDB annotations including experimental details, source organisms, classification, and biological context
• Extended Description from DrugBank: Inclusion of 871 samples with additional detailed textual information about the corresponding antibodies, offering deeper context for advanced analysis.
• Binding affinity measurements (ΔG values) for 411 entries from SAbDab
• Multi-modal integration combining sequences, structures, and natural language descriptions

Associated Paper

TeBaAb: Text-Based Antigen-Conditioned Antibody Redesign via Directed Evolution

Project Page / Source Code: https://github.com/HySonLab/TeBaAb

This dataset was created to support the TeBaAb framework, which combines:

• Conditional Variational Autoencoder (CVAE) jointly conditioned on antigen sequences and textual descriptions
• Two-stage binding affinity predictor
• Iterative enrichment loop inspired by directed evolution

Key Results: TeBaAb improves predicted binding affinity by an average of 10% compared to original antibodies while preserving structural confidence (RMSPE < 1.0Å).

Dataset Structure

Data Fields

• heavy_chain (string): Complete amino acid sequence of the antibody heavy chain
• light_chain (string): Complete amino acid sequence of the antibody light chain
• antigen (string): Target antigen sequence(s). Multiple antigen fragments are separated by / following SAbDab conventions
• description (string): Free-text description of antibody properties including:
  • Protein classification
  • Source organism
  • Expression system
  • Experimental details
  • Structural characteristics
  • Symmetry information
• drugbank_description (string, optional): Extended, detailed textual description of the antibody collected from the DrugBank database. Available for a subset of 871 entries.
• delta_g (float, optional): Binding free energy in kJ/mol (lower values indicate stronger binding). Available for 411 entries
• pdb_id (string): Protein Data Bank identifier for accessing structural data

Data Splits

The dataset contains:

• train: 7,684 complete antibody-antigen pairs with sequences and descriptions

Dataset Creation

Source Data

AbDes was curated from multiple authoritative sources:

• AbSet: Foundation dataset providing antibody structures and molecular descriptors
• Protein Data Bank (PDB): Structural annotations and experimental metadata
• SAbDab (Structured Antibody Database): Binding affinity measurements (ΔG values)
• DrugBank: Source for extended textual descriptions related to the therapeutic context of the antibodies.

Curation Process

1. Filtering: Selected 7,684 complete entries from AbSet with paired heavy/light chains and antigen information
2. Text annotation: Systematically collected descriptive metadata from PDB entries using PDB IDs
3. Text annotation (DrugBank): Integrated a set of 871 extended descriptions from DrugBank using PDB ID mapping and cleaned the text for direct use.
4. Affinity integration: Merged experimentally determined ΔG values from SAbDab for applicable entries
5. Quality control: Validated sequence completeness and description quality

Usage

Loading the Dataset

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("hysonlab/abdes")

Basic Usage Example

# Iterate through training examples
for example in dataset["train"]:
    heavy = example["heavy_chain"]
    light = example["light_chain"]
    antigen = example["antigen"]
    description = example["description"]

    # Process sequences and text
    print(f"Description: {description}")
    print(f"Binding affinity: {example.get('delta_g', 'N/A')}")

Use Cases

1. Text-conditioned antibody design: Train generative models that incorporate natural language descriptions
2. Binding affinity prediction: Develop models to predict antibody-antigen binding strength
3. Multi-modal learning: Research at the intersection of protein sequences and natural language
4. Therapeutic antibody discovery: Accelerate drug development through AI-driven design
5. Structural bioinformatics: Study relationships between sequence, structure, and function

Citation

If you use this dataset, please cite:

@article{tebaab2025,
  title={TeBaAb: Text-Based Antigen-Conditioned Antibody Redesign via Directed Evolution},
  author={[Your Name(s)]},
  journal={[Journal/Conference]},
  year={2025}
}

Dataset Statistics

Statistic	Value
Total entries	7,684
Entries with ΔG	411
Entries with DrugBank Desc	871
Average heavy chain length	121.22
Average light chain length	107.95
Average description length	294.60
Unique PDB entries	7,684
Source organisms	Multiple (primarily mouse, human)
Expression systems	Multiple (E. coli, mammalian, yeast)

License

This dataset is released under the CC-BY-4.0 license. Users are free to share and adapt the material with appropriate credit to the original sources (PDB, SAbDab, AbSet, DrugBank).

Acknowledgments

We acknowledge the following resources:

• Protein Data Bank (PDB) for structural and annotation data
• SAbDab for binding affinity measurements
• AbSet for the foundational antibody-antigen dataset
• DrugBank for providing extended drug/antibody description metadata.

Contact

For questions, issues, or collaborations, please open an issue or contact dodo5.

Version History

• v1.1 (2025-11): Added drugbank_description feature with 871 extended entries.
• v1.0 (2025-11): Initial release with 7,684 entries