| --- |
| 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", |
| ) |
| ``` |
|
|
|
|
