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Assay-aware-BindingDB

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protein-ligand
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metadata 
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: &ref_0
      - 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: *ref_0
  - config_name: spr
    data_files:
      - split: full
        path: data/spr.jsonl
    features: *ref_0
  - config_name: rba
    data_files:
      - split: full
        path: data/rba.jsonl
    features: *ref_0
  - config_name: fpa
    data_files:
      - split: full
        path: data/fpa.jsonl
    features: *ref_0
  - config_name: itc_seed_0
    data_dir: data/splits/itc/seed_0
    features: *ref_0
  - config_name: itc_seed_1
    data_dir: data/splits/itc/seed_1
    features: *ref_0
  - config_name: itc_seed_2
    data_dir: data/splits/itc/seed_2
    features: *ref_0
  - config_name: itc_seed_3
    data_dir: data/splits/itc/seed_3
    features: *ref_0
  - config_name: itc_seed_4
    data_dir: data/splits/itc/seed_4
    features: *ref_0
  - config_name: itc_seed_5
    data_dir: data/splits/itc/seed_5
    features: *ref_0
  - config_name: itc_seed_6
    data_dir: data/splits/itc/seed_6
    features: *ref_0
  - config_name: itc_seed_7
    data_dir: data/splits/itc/seed_7
    features: *ref_0
  - config_name: itc_seed_8
    data_dir: data/splits/itc/seed_8
    features: *ref_0
  - config_name: itc_seed_9
    data_dir: data/splits/itc/seed_9
    features: *ref_0
  - config_name: spr_seed_0
    data_dir: data/splits/spr/seed_0
    features: *ref_0
  - config_name: spr_seed_1
    data_dir: data/splits/spr/seed_1
    features: *ref_0
  - config_name: spr_seed_2
    data_dir: data/splits/spr/seed_2
    features: *ref_0
  - config_name: spr_seed_3
    data_dir: data/splits/spr/seed_3
    features: *ref_0
  - config_name: spr_seed_4
    data_dir: data/splits/spr/seed_4
    features: *ref_0
  - config_name: spr_seed_5
    data_dir: data/splits/spr/seed_5
    features: *ref_0
  - config_name: spr_seed_6
    data_dir: data/splits/spr/seed_6
    features: *ref_0
  - config_name: spr_seed_7
    data_dir: data/splits/spr/seed_7
    features: *ref_0
  - config_name: spr_seed_8
    data_dir: data/splits/spr/seed_8
    features: *ref_0
  - config_name: spr_seed_9
    data_dir: data/splits/spr/seed_9
    features: *ref_0
  - config_name: rba_seed_0
    data_dir: data/splits/rba/seed_0
    features: *ref_0
  - config_name: rba_seed_1
    data_dir: data/splits/rba/seed_1
    features: *ref_0
  - config_name: rba_seed_2
    data_dir: data/splits/rba/seed_2
    features: *ref_0
  - config_name: rba_seed_3
    data_dir: data/splits/rba/seed_3
    features: *ref_0
  - config_name: rba_seed_4
    data_dir: data/splits/rba/seed_4
    features: *ref_0
  - config_name: rba_seed_5
    data_dir: data/splits/rba/seed_5
    features: *ref_0
  - config_name: rba_seed_6
    data_dir: data/splits/rba/seed_6
    features: *ref_0
  - config_name: rba_seed_7
    data_dir: data/splits/rba/seed_7
    features: *ref_0
  - config_name: rba_seed_8
    data_dir: data/splits/rba/seed_8
    features: *ref_0
  - config_name: rba_seed_9
    data_dir: data/splits/rba/seed_9
    features: *ref_0
  - config_name: fpa_seed_0
    data_dir: data/splits/fpa/seed_0
    features: *ref_0
  - config_name: fpa_seed_1
    data_dir: data/splits/fpa/seed_1
    features: *ref_0
  - config_name: fpa_seed_2
    data_dir: data/splits/fpa/seed_2
    features: *ref_0
  - config_name: fpa_seed_3
    data_dir: data/splits/fpa/seed_3
    features: *ref_0
  - config_name: fpa_seed_4
    data_dir: data/splits/fpa/seed_4
    features: *ref_0
  - config_name: fpa_seed_5
    data_dir: data/splits/fpa/seed_5
    features: *ref_0
  - config_name: fpa_seed_6
    data_dir: data/splits/fpa/seed_6
    features: *ref_0
  - config_name: fpa_seed_7
    data_dir: data/splits/fpa/seed_7
    features: *ref_0
  - config_name: fpa_seed_8
    data_dir: data/splits/fpa/seed_8
    features: *ref_0
  - config_name: fpa_seed_9
    data_dir: data/splits/fpa/seed_9
    features: *ref_0

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:

pip install datasets

Load the full dataset 

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:

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 

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 

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:

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:

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:

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:

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",
)