Datasets:

EdisonScientific
/

DMPKBench

license: cc-by-4.0
language:
  - en
pretty_name: DMPKBench
tags:
  - dmpk
  - admet
  - pharmacokinetics
  - pk
  - chemistry
  - benchmark
  - qa
configs:
  - config_name: ADMET-MPO
    data_files:
      - split: train
        path: ADMET-MPO/train.parquet
      - split: val
        path: ADMET-MPO/val.parquet
      - split: test
        path: ADMET-MPO/test.parquet
  - config_name: pk-interpretation
    data_files:
      - split: train
        path: pk-interpretation/train.parquet
      - split: val
        path: pk-interpretation/val.parquet
      - split: test
        path: pk-interpretation/test.parquet

DMPKBench

Edison packaging of the public DMPKBench subsets for agent evaluation.

Two Hugging Face configs (subsets), each with train / val / test splits at ≈70/15/15 (seeded shuffle, seed=42; remainder assigned to train):

subset	train	val	test	total
`ADMET-MPO`	456	97	97	650
`pk-interpretation`	71	14	14	99

Subsets

`ADMET-MPO`

Multiple-choice ADMET/MPO compound-selection questions from ADMET-MPO.json. Each row asks which of four SMILES is most consistent with a public experimental assay outcome. Graded with a single answer rubric criterion (correct option / SMILES). params stores JSON domain, answer, answer_smiles, and source_id.

`pk-interpretation`

Open-answer PK curve interpretation questions derived from Result_Interpretation.json. Options are hidden; agents must answer directly and explain using the provided PK data. Graded with two rubric criteria (answer, explanation).

Schema (one row per question)

column	notes
`uuid`	stable primary key
`question_id`	`dmpkbench_admet_mpo_<id>` or `dmpkbench_result_interp_<id>`
`provenance`	`DMPKBench`
`task_type`	`QA`
`grader_type`	`rubric`
`question`	full self-contained prompt
`input_files` / `data_storage_uris`	unused (empty)
`reference_file` / `reference_storage_uris`	unused (empty)
`params`	optional JSON (ADMET domain / gold letter / SMILES)
`rubric`	LLM-judge criteria

Load

from datasets import load_dataset

admet = load_dataset("EdisonScientific/DMPKBench", "ADMET-MPO", split="test")
pk = load_dataset("EdisonScientific/DMPKBench", "pk-interpretation", split="test")