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 dataset is provided as one unsplit full split. It is intended for assay-aware binding-affinity analysis, information extraction, retrieval, and dataset curation. It does not include predefined training, validation, or test partitions.

Assay composition

Data schema

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):,}")

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))

Filter by assay

itc = dataset.filter(lambda row: row["assay_type"] == "itc")
print(f"ITC records: {len(itc):,}")