Datasets:

anonymousapple
/

Assay-aware-BindingDB

---
pretty_name: Assay-aware BindingDB
tags:
  - binding-affinity
  - drug-discovery
  - protein-ligand
  - biophysics
  - chemistry
  - biology
  - tabular
  - datasets
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    default: true
    data_files:
      - split: full
        path:
          - data/itc.jsonl
          - data/spr.jsonl
          - data/fpa.jsonl
          - data/rba.jsonl
    features: &bindingdb_features
      - name: reactant_set_id
        dtype: int64
      - name: pmid
        dtype: int64
      - name: protein
        dtype: string
      - name: ligand
        struct:
          - name: smiles
            dtype: string
      - name: affinity_data
        struct:
          - name: type
            dtype: string
          - name: value
            dtype: float64
          - name: relation
            dtype: string
          - name: unit
            dtype: string
      - name: DESCRIPTION
        dtype: string
      - name: search_path
        sequence: string
      - name: supplementary_source
        sequence: string
      - name: references_previous
        dtype: string
      - name: original_paragraph
        dtype: string
      - name: structured_description
        dtype: string
      - name: assay_type
        dtype: string
      - name: source_filename
        dtype: string
      - name: source_record_key
        dtype: string
  - config_name: itc
    data_files:
      - split: full
        path: data/itc.jsonl
    features: *bindingdb_features
  - config_name: spr
    data_files:
      - split: full
        path: data/spr.jsonl
    features: *bindingdb_features
  - config_name: rba
    data_files:
      - split: full
        path: data/rba.jsonl
    features: *bindingdb_features
  - config_name: fpa
    data_files:
      - split: full
        path: data/fpa.jsonl
    features: *bindingdb_features
  - config_name: itc_seed_0
    data_dir: data/splits/itc/seed_0
    features: *bindingdb_features
  - config_name: itc_seed_1
    data_dir: data/splits/itc/seed_1
    features: *bindingdb_features
  - config_name: itc_seed_2
    data_dir: data/splits/itc/seed_2
    features: *bindingdb_features
  - config_name: itc_seed_3
    data_dir: data/splits/itc/seed_3
    features: *bindingdb_features
  - config_name: itc_seed_4
    data_dir: data/splits/itc/seed_4
    features: *bindingdb_features
  - config_name: itc_seed_5
    data_dir: data/splits/itc/seed_5
    features: *bindingdb_features
  - config_name: itc_seed_6
    data_dir: data/splits/itc/seed_6
    features: *bindingdb_features
  - config_name: itc_seed_7
    data_dir: data/splits/itc/seed_7
    features: *bindingdb_features
  - config_name: itc_seed_8
    data_dir: data/splits/itc/seed_8
    features: *bindingdb_features
  - config_name: itc_seed_9
    data_dir: data/splits/itc/seed_9
    features: *bindingdb_features
  - config_name: spr_seed_0
    data_dir: data/splits/spr/seed_0
    features: *bindingdb_features
  - config_name: spr_seed_1
    data_dir: data/splits/spr/seed_1
    features: *bindingdb_features
  - config_name: spr_seed_2
    data_dir: data/splits/spr/seed_2
    features: *bindingdb_features
  - config_name: spr_seed_3
    data_dir: data/splits/spr/seed_3
    features: *bindingdb_features
  - config_name: spr_seed_4
    data_dir: data/splits/spr/seed_4
    features: *bindingdb_features
  - config_name: spr_seed_5
    data_dir: data/splits/spr/seed_5
    features: *bindingdb_features
  - config_name: spr_seed_6
    data_dir: data/splits/spr/seed_6
    features: *bindingdb_features
  - config_name: spr_seed_7
    data_dir: data/splits/spr/seed_7
    features: *bindingdb_features
  - config_name: spr_seed_8
    data_dir: data/splits/spr/seed_8
    features: *bindingdb_features
  - config_name: spr_seed_9
    data_dir: data/splits/spr/seed_9
    features: *bindingdb_features
  - config_name: rba_seed_0
    data_dir: data/splits/rba/seed_0
    features: *bindingdb_features
  - config_name: rba_seed_1
    data_dir: data/splits/rba/seed_1
    features: *bindingdb_features
  - config_name: rba_seed_2
    data_dir: data/splits/rba/seed_2
    features: *bindingdb_features
  - config_name: rba_seed_3
    data_dir: data/splits/rba/seed_3
    features: *bindingdb_features
  - config_name: rba_seed_4
    data_dir: data/splits/rba/seed_4
    features: *bindingdb_features
  - config_name: rba_seed_5
    data_dir: data/splits/rba/seed_5
    features: *bindingdb_features
  - config_name: rba_seed_6
    data_dir: data/splits/rba/seed_6
    features: *bindingdb_features
  - config_name: rba_seed_7
    data_dir: data/splits/rba/seed_7
    features: *bindingdb_features
  - config_name: rba_seed_8
    data_dir: data/splits/rba/seed_8
    features: *bindingdb_features
  - config_name: rba_seed_9
    data_dir: data/splits/rba/seed_9
    features: *bindingdb_features
  - config_name: fpa_seed_0
    data_dir: data/splits/fpa/seed_0
    features: *bindingdb_features
  - config_name: fpa_seed_1
    data_dir: data/splits/fpa/seed_1
    features: *bindingdb_features
  - config_name: fpa_seed_2
    data_dir: data/splits/fpa/seed_2
    features: *bindingdb_features
  - config_name: fpa_seed_3
    data_dir: data/splits/fpa/seed_3
    features: *bindingdb_features
  - config_name: fpa_seed_4
    data_dir: data/splits/fpa/seed_4
    features: *bindingdb_features
  - config_name: fpa_seed_5
    data_dir: data/splits/fpa/seed_5
    features: *bindingdb_features
  - config_name: fpa_seed_6
    data_dir: data/splits/fpa/seed_6
    features: *bindingdb_features
  - config_name: fpa_seed_7
    data_dir: data/splits/fpa/seed_7
    features: *bindingdb_features
  - config_name: fpa_seed_8
    data_dir: data/splits/fpa/seed_8
    features: *bindingdb_features
  - config_name: fpa_seed_9
    data_dir: data/splits/fpa/seed_9
    features: *bindingdb_features
---

# Assay-aware BindingDB

Assay-aware BindingDB is a collection of protein–ligand binding
records organized by experimental assay type. Each row represents a BindingDB
reactant set and includes its measured affinity, source publication, original
experimental context, and an assay-specific structured description.

The complete dataset remains available as the `full` split. Four assay
configurations provide direct access to ITC, SPR, FPA, or RBA records, and 40
training-compatible configurations provide predefined training, validation,
and test partitions for seeds 0 through 9.

## Configurations and splits

The `default` configuration contains all four assays in one `full` split.
The `itc`, `spr`, `rba`, and `fpa` configurations each expose one complete
assay as `full` without applying training eligibility filters.

Configurations named `<assay>_seed_<seed>` reproduce the data used in the downstream binding affinity prediction experiments for seeds 0–9 and expose `train`, `validation`, and `test` splits.

Eligible records require:

- A precomputed Boltz-2 affinity representation.
- A Qwen3 assay-context embedding.
- Exactly one positive numeric Kd, Ki, or IC50 value.
- No `<` or `>` qualifier.

Unique PMIDs are shuffled with NumPy `RandomState(seed)`. Using integer truncation, 20% are assigned to test, 10% to validation, and the remainder to train, preventing PMID leakage.


## Data schema

| Field | Type | Description |
|---|---|---|
| `reactant_set_id` | integer | BindingDB reactant-set identifier and primary record identifier. |
| `pmid` | integer | PubMed identifier for the source publication. |
| `protein` | string | Protein or biological target name. |
| `ligand.smiles` | string | Ligand structure represented as SMILES. |
| `affinity_data.type` | string | Measurement type, such as `Kd`, `Ki`, or `IC50`. |
| `affinity_data.value` | float | Numeric affinity value. |
| `affinity_data.relation` | string | Reported comparison operator, such as `=`, `<`, or `>`. |
| `affinity_data.unit` | string | Unit associated with the affinity value. |
| `DESCRIPTION` | string | BindingDB assay description. |
| `assay_type` | string | One of `itc`, `spr`, `fpa`, or `rba`. |
| `search_path` | list of strings | Locations searched when extracting experimental context. |
| `supplementary_source` | list of strings | Supplementary sources used during extraction. |
| `references_previous` | string, nullable | Relevant preceding references captured from the publication. |
| `original_paragraph` | JSON string, nullable | Source passages serialized as JSON. |
| `structured_description` | JSON string, nullable | Assay-aware structured extraction serialized as JSON. |
| `source_filename` | string | Name of the source JSON file. |
| `source_record_key` | string | Original record key in the source file. |

`original_paragraph` and `structured_description` are JSON-encoded strings
rather than nested Arrow objects because their internal structures vary among
publications and assay types. They can be decoded into Python objects when
nested data is needed. Both fields are nullable.

## Examples

Install the Hugging Face Datasets library before running the examples:

```bash
pip install datasets
```

### Load the full dataset

```python
from datasets import load_dataset

dataset = load_dataset(
    "anonymousapple/Assay-aware-BindingDB",
    split="full",
)

print(dataset)
print(f"Number of records: {len(dataset):,}")
```

### Load one complete assay

Pass the assay configuration name as the second argument:

```python
from datasets import load_dataset

dataset = load_dataset(
    "anonymousapple/Assay-aware-BindingDB",
    "itc",
    split="full",
)

print(f"ITC records: {len(dataset)}")
```

### Read an individual record

```python
record = dataset[0]

print("Reactant set:", record["reactant_set_id"])
print("PMID:", record["pmid"])
print("Assay:", record["assay_type"])
print("Protein:", record["protein"])
print("Ligand SMILES:", record["ligand"]["smiles"])

affinity = record["affinity_data"]
print(
    "Affinity:",
    affinity["type"],
    affinity["relation"],
    affinity["value"],
    affinity["unit"],
)
```

### Read multiple records

```python
for record in dataset.select(range(5)):
    print(
        record["reactant_set_id"],
        record["protein"],
        record["assay_type"],
    )
```

### Read structured descriptions

Use `json.loads()` to decode the JSON string and `json.dumps()` with
indentation to display it in a readable structure:

```python
import json

record = dataset[0]
value = record["structured_description"]

if value is not None:
    structured_description = json.loads(value)
    print(
        json.dumps(
            structured_description,
            indent=2,
            ensure_ascii=False,
        )
    )
```

After decoding, nested values can be accessed normally:

```python
if record["structured_description"] is not None:
    structured_description = json.loads(
        record["structured_description"]
    )
    print(json.dumps(structured_description, indent=2, ensure_ascii=False))


```

The same approach works for `original_paragraph`:

```python
value = record["original_paragraph"]

if value is not None:
    original_paragraph = json.loads(value)
    print(json.dumps(original_paragraph, indent=2, ensure_ascii=False))
```

### Load a training-compatible seeded split

Combine the assay and split seed in the configuration name, then select a
normal Hugging Face split:

```python
from datasets import load_dataset

itc_train_seed_1 = load_dataset(
    "anonymousapple/Assay-aware-BindingDB",
    "itc_seed_1",
    split="train",
)

itc_validation_seed_1 = load_dataset(
    "anonymousapple/Assay-aware-BindingDB",
    "itc_seed_1",
    split="validation",
)

itc_test_seed_1 = load_dataset(
    "anonymousapple/Assay-aware-BindingDB",
    "itc_seed_1",
    split="test",
)
```