license: cc-by-4.0
language:
- en
pretty_name: DrugDiscoveryBench
tags:
- drug-discovery
- biology
- chemistry
- benchmark
- agent
extra_gated_prompt: >
This dataset contains evaluation-only ground truth and rubrics. Do not
redistribute them or use them to train or fine-tune models to game the
benchmark. By requesting access you agree to use the protected fields only for
evaluation.
configs:
- config_name: default
data_files:
- split: train
path: train.parquet
DrugDiscoveryBench
Authorized Edison packaging of ScaleAI/DrugDiscoveryBench for agent evaluation. It contains the benchmark's 82 tasks in one train split and preserves the source's evaluation-only access conditions.
Source revision: 10cbbbb5da6f0fa46a6567c7cae0cbb3baa6c7cc.
Capability counts
| capability | tasks |
|---|---|
cheminformatics |
10 |
database_screening |
14 |
molecular_biology |
7 |
patent_mining |
13 |
sar_affinity |
7 |
structural_reasoning |
19 |
target_id_genetics |
12 |
Schema
Each row contains a stable uuid, source question_id and source_category, the agent-visible question, grader_type, protected ground_truth and source metadata in params, and the normalized rubric. input_files is a compact JSON list of task-local filenames. data_storage_uris is a list of matching DEV DSS data_entry: URIs in the same order. The rubric's criteria have stable IDs, correctness or methodology category, and signed numeric point weights.
Outcome criteria use IDs outcome_NNN_positive or outcome_NNN_negative; process criteria use the equivalent process_NNN_* IDs. The suffix is the criterion's signed-point polarity: positive weights award points when met, while negative weights deduct points when the detrimental condition is met.
Load
from datasets import load_dataset
tasks = load_dataset("EdisonScientific/DrugDiscoveryBench", split="train")