Duplicate from OpenMed/agab-db

Co-authored-by: Maziyar Panahi <MaziyarPanahi@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: dataset
+    dtype: string
+  - name: heavy_sequence
+    dtype: string
+  - name: light_sequence
+    dtype: string
+  - name: scfv
+    dtype: bool
+  - name: affinity_type
+    dtype: string
+  - name: affinity
+    dtype: string
+  - name: antigen_sequence
+    dtype: string
+  - name: confidence
+    dtype:
+      class_label:
+        names:
+          '0': medium
+          '1': high
+          '2': very_high
+  - name: nanobody
+    dtype: bool
+  - name: processed_measurement
+    dtype: float64
+  - name: target_name
+    dtype: string
+  - name: target_pdb
+    dtype: string
+  - name: target_uniprot
+    dtype: string
+  - name: source_url
+    dtype: string
+  - name: heavy_cdr1
+    dtype: string
+  - name: heavy_cdr2
+    dtype: string
+  - name: heavy_cdr3
+    dtype: string
+  - name: light_cdr1
+    dtype: string
+  - name: light_cdr2
+    dtype: string
+  - name: light_cdr3
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2137958513
+    num_examples: 1227083
+  download_size: 339997839
+  dataset_size: 2137958513
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+pretty_name: 'AgAb DB: Antigen Specific Antibody Database'
+tags:
+- biology
+- immunology
+- antibodies
+- protein-protein-interactions
+- drug-discovery
+- computational-biology
+- therapeutics
+- machine-learning
+- protein-sequence-modeling
+- binding-affinity-prediction
+- antibody-design
+task_categories:
+- text-classification
+license: other
+license_details: "Non-commercial research use only. Commercial inquiries should be directed to NaturalAntibody."
+language:
+  - en
+---
+
+# AgAb DB: Antigen Specific Antibody Database
+
+A comprehensive collection of antibody-antigen interaction data for computational biology and therapeutic design.
+
+## Dataset Summary
+
+AgAb DB aggregates antibody-antigen binding data from multiple sources, containing over 1.2 million antibody-antigen pairs with binding affinity measurements. This dataset is essential for training machine learning models in computational immunology and antibody engineering.
+
+## Key Statistics
+
+- **1,227,083** antibody-antigen interaction records
+- **309,884** unique antibodies (full antibodies, nanobodies, scFvs)
+- **4,334** unique antigens
+- **170,660** complete heavy/light chain pairs
+- **70,388** nanobodies and **132,157** scFv antibodies
+- **Focus on human health**: Infectious diseases, cancer, autoimmune conditions
+- **Diverse antigen types**: Viral proteins, bacterial antigens, cancer markers, autoantigens
+
+*Note: Statistics for unique antibodies/antigens are from original documentation and may be proportionally larger in the full 1.2M record dataset.*
+
+### Data Quality Distribution
+
+- **51% very_high confidence** (robust sequences and methodology)
+- **high confidence** (manually curated datasets)
+- **medium confidence** (automated discovery, some uncertainty)
+
+### Affinity Measurement Types
+
+- Quantitative metrics: Gibbs free energy changes, kinetic constants, IC₅₀
+- Qualitative binding assessments
+- Mixed data types across different sources
+
+## Data Structure
+
+### Core Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `heavy_sequence` | string | Antibody heavy chain amino acid sequence |
+| `light_sequence` | string | Antibody light chain amino acid sequence |
+| `antigen_sequence` | string | Target antigen amino acid sequence |
+| `affinity` | string | Binding affinity value |
+| `confidence` | string | Data quality level (very_high, high, medium) |
+
+### Additional Metadata
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `dataset` | string | Original source dataset |
+| `affinity_type` | string | Measurement type (KD, IC₅₀, etc.) |
+| `nanobody` | bool | Whether it's a nanobody |
+| `scfv` | bool | Single-chain variable fragment |
+| `target_name` | string | Antigen name |
+| `target_pdb` | string | PDB structure ID |
+| `target_uniprot` | string | UniProt accession |
+| `heavy_cdr1/cdr2/cdr3` | string | Complementarity-determining regions |
+| `light_cdr1/cdr2/cdr3` | string | Light chain CDRs |
+
+## Dataset Split
+
+- **Train**: All 1,227,083 records in a single training set
+
+The full dataset is provided as a single training split to maximize available data for machine learning applications. Users can create their own validation/test splits as needed for their specific use cases.
+
+### Confidence Categories
+
+- **very_high**: Both sequences and methodology used for calculating affinity were robust (e.g., AbDesign, BioMap, SKEMPI 2.0)
+- **high**: Manually curated datasets or those containing antigen names/mutations rather than full sequences (e.g., FLAB datasets)
+- **medium**: Automated data discovery with some uncertainty (e.g., patent databases)
+
+### Antibody Types Included
+
+- **Full antibodies**: Complete heavy and light chain pairs (traditional monoclonal antibodies)
+- **Nanobodies**: Single-domain antibodies (VHH format) - 70K+ entries across datasets
+- **scFv**: Single-chain variable fragments - 132K+ entries, primarily from AlphaSeq
+- **Mixed formats**: Various antibody fragment types and engineered variants
+
+### Nanobody Distribution by Source
+
+| Source | Nanobody Count | Notes |
+|--------|----------------|-------|
+| AlphaSeq | 67,058 | Mutations for improved binding |
+| Patents | 40,517 | Patent literature extraction |
+| Literature | 1,936 | Research paper curation |
+| Structures | 1,258 | PDB structure-derived |
+| AATP, OSH, RMNA | ~133 | Specialized datasets |
+
+### scFv Distribution by Source
+
+| Source | scFv Count | Notes |
+|--------|------------|-------|
+| AlphaSeq | 131,645 | Primary scFv source |
+| Literature | 512 | Research paper curation |
+
+### Sequence Characteristics
+
+- **Predominantly short sequences**: <150 amino acids typical
+- **Majority include both chains**: Heavy and light chain pairs
+- **Diverse antigen targets**: Infectious diseases, cancer, autoimmune conditions
+- **Multiple affinity measurement types**: KD, IC₅₀, ΔG, binary binding
+
+## Usage
+
+### Load the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load from OpenMed
+dataset = load_dataset("OpenMed/agab-db")
+
+# Access the training data (full dataset)
+train_data = dataset["train"]
+
+# Optional: Create your own validation/test splits
+from sklearn.model_selection import train_test_split
+import pandas as pd
+
+# Convert to pandas for splitting
+df = pd.DataFrame(train_data)
+train_df, test_df = train_test_split(df, test_size=0.1, random_state=42)
+train_df, val_df = train_test_split(train_df, test_size=0.1, random_state=42)
+```
+
+### Filter for Research
+
+```python
+# High-quality data only
+high_quality = dataset.filter(lambda x: x["confidence"] == "very_high")
+
+# Nanobodies for specialized studies
+nanobodies = dataset.filter(lambda x: x["nanobody"] == True)
+
+# Specific antigens
+covid_data = dataset.filter(lambda x: "covid" in x["target_name"].lower())
+```
+
+### Prepare for ML Training
+
+```python
+# Extract sequences for language models
+sequences = []
+for item in dataset["train"]:
+    if item["heavy_sequence"]:
+        sequences.append(item["heavy_sequence"])
+    if item["light_sequence"]:
+        sequences.append(item["light_sequence"])
+```
+
+## Applications
+
+### Machine Learning Use Cases
+
+- **Antibody language models**: Train sequence models on antibody repertoires for generative design
+- **Binding affinity prediction**: Develop regression models for antibody-antigen interaction strength
+- **Therapeutic design**: Guide rational antibody engineering and optimization
+- **Computational immunology**: Study immune responses and antibody development patterns
+- **Virtual screening**: Prioritize antibody candidates for experimental validation
+- **Structure-affinity relationships**: Learn connections between 3D structures and binding properties
+
+### Research Applications
+
+- **Antibody repertoire analysis**: Study natural antibody diversity and evolution
+- **Cross-reactivity prediction**: Identify potential off-target effects
+- **Immunogenicity assessment**: Predict antibody developability and safety
+- **Drug discovery pipelines**: Accelerate hit identification and lead optimization
+- **Comparative immunology**: Study antibody responses across different species
+
+### Integration with Other Tools
+
+- **Protein structure prediction**: Use with ESMFold for 3D structure generation
+- **Molecular dynamics**: Combine with simulation tools for binding mechanism studies
+- **High-throughput screening**: Guide experimental antibody library screening
+- **CRISPR engineering**: Design antibodies for gene therapy applications
+
+## Data Sources
+
+Aggregated from 25+ datasets including GenBank, SKEMPI 2.0, peer-reviewed publications, and patent databases.
+
+### Major Dataset Components
+
+| Dataset | Records | Unique Antibodies | Key Characteristics |
+|---------|---------|-------------------|-------------------|
+| **BUZZ** | 524,346 | 524,346 | Trastuzumab mutations binding to HER2 |
+| **AlphaSeq** | 198,703 | 193,867 | Antibody mutations across 4 targets (TIGIT, SARS-CoV2-RBD, PD-1, HER2) |
+| **ABBD** | 155,853 | 88,946 | Eight antibody-antigen cases with heavy chain mutations |
+| **Patents** | 217,463 | 31,173 | NLP-extracted sequences from patent literature |
+| **COVID-19** | 27,301 | 6,759 | SARS-CoV-2 neutralization data (Cov-AbDab) |
+| **HIV** | 48,008 | 192 | HIV-targeting antibodies (LANL database) |
+| **BioMap** | 2,725 | 728 | Binding ΔG values across 8 species |
+| **Literature** | 5,580 | 4,841 | Curated from research articles (1,940 nanobodies) |
+| **FLAB** | 6,849 | 6,798 | Five publications on viral/cancer targets |
+| **ABDesign** | 672 | 672 | Systematic CDR-H3 point mutations |
+
+### Inclusion Criteria
+
+- Transparency and completeness of data
+- Relevance to human health
+- Quantitative binding affinity measurements
+- Complete amino acid sequences for all biomolecules
+
+### Data Processing Pipeline
+
+1. **Aggregation**: Collection from 14 distinct sources → 25 integrated datasets
+2. **Curation**: Multi-stage pipeline with automated extraction, normalization, and manual verification
+3. **Standardization**: Common structure implemented across all studies
+4. **Validation**: Automated feasibility checks and manual verification of critical datasets
+
+## Citation
+
+```bibtex
+@dataset{agab_db,
+  title={AgAb DB: Antigen Specific Antibody Database},
+  author={NaturalAntibody},
+  year={2024},
+  url={https://naturalantibody.com/agab/}
+}
+```
+
+## License
+
+Available for non-commercial research use only. Contact NaturalAntibody for commercial licensing.
+
+---
+
+*Dataset provided by [NaturalAntibody](https://naturalantibody.com/agab/)*
+

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