Restructure as DMPKBench with ADMET-MPO and pk-interpretation subsets

data/train-00000-of-00001.parquet → ADMET-MPO/test.parquet

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ADMET-MPO/train.parquet

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ADMET-MPO/val.parquet

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README.md

@@ -1,45 +1,65 @@
 ---
-dataset_info:
-  features:
-  - name: uuid
-    dtype: large_string
-  - name: question_id
-    dtype: large_string
-  - name: provenance
-    dtype: large_string
-  - name: task_type
-    dtype: large_string
-  - name: grader_type
-    dtype: large_string
-  - name: question
-    dtype: large_string
-  - name: input_files
-    dtype: large_string
-  - name: data_storage_uris
-    list: string
-  - name: reference_file
-    dtype: large_string
-  - name: reference_storage_uris
-    list: string
-  - name: params
-    dtype: large_string
-  - name: rubric
-    list:
-    - name: criterion_id
-      dtype: string
-    - name: description
-      dtype: string
-    - name: weight
-      dtype: float64
-  splits:
-  - name: train
-    num_bytes: 119883
-    num_examples: 99
-  download_size: 77466
-  dataset_size: 119883
+license: cc-by-4.0
+language: [en]
+pretty_name: DMPKBench
+tags: [dmpk, admet, pharmacokinetics, pk, chemistry, benchmark, qa]
 configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+  - 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](https://github.com/GHDDI-AILab/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`](https://github.com/GHDDI-AILab/DMPKBench/blob/main/public%20subset/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`](https://github.com/GHDDI-AILab/DMPKBench/blob/main/public%20subset/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
+
+```python
+from datasets import load_dataset
+
+admet = load_dataset("EdisonScientific/DMPKBench", "ADMET-MPO", split="test")
+pk = load_dataset("EdisonScientific/DMPKBench", "pk-interpretation", split="test")
+```

pk-interpretation/test.parquet

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pk-interpretation/train.parquet

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pk-interpretation/val.parquet

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