| --- |
| pretty_name: PKPD Dataset |
| language: |
| - en |
| task_categories: |
| - text-generation |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - extended |
| annotations_creators: |
| - machine-generated |
| --- |
| |
| # PKPD Dataset |
|
|
| ## Dataset summary |
|
|
| This dataset is a pharmacokinetics/pharmacodynamics (PK/PD) and pharmacometrics corpus built for domain-adaptive pretraining (DAPT). |
|
|
| It was created from automatically downloadable biomedical literature using: |
|
|
| - PubMed search via NCBI E-utilities |
| - PMID to PMCID mapping via the official PMC id conversion service |
| - Europe PMC / PMC open-access full-text XML retrieval |
| - JATS/XML parsing and heuristic filtering |
|
|
| The current released corpus contains **27,990 documents** and approximately **109.4 million estimated tokens**. |
|
|
| This release contains only the **core PubMed/PMC open-access article corpus**. Optional FDA guidance and open-source repository documentation were implemented in the pipeline but are **not included** in the current dataset export. |
|
|
| ## Scope |
|
|
| The search strategy targets: |
|
|
| - pharmacokinetics |
| - pharmacodynamics |
| - PK/PD modeling |
| - population PK/PD |
| - nonlinear mixed effects modeling |
| - NONMEM / Monolix / SAEM / FOCE / NLME |
| - PBPK |
| - exposure-response |
| - dose selection |
| - model-informed drug development |
| - clinical pharmacology |
| - covariate modeling |
| - Bayesian PKPD |
|
|
| The corpus is intended for: |
|
|
| - domain-adaptive pretraining |
| - continued pretraining of biomedical or general LLMs |
| - information retrieval / RAG experiments |
| - corpus analysis for pharmacometrics language |
|
|
| ## Source data and collection pipeline |
|
|
| ### Source systems |
|
|
| Primary source systems used for this release: |
|
|
| 1. **PubMed / NCBI E-utilities** |
| 2. **PMC ID conversion API** |
| 3. **Europe PMC fullTextXML endpoint** |
|
|
| Excluded from this release: |
|
|
| - paywalled journal scraping |
| - copyrighted textbook scraping |
| - FDA guidance pages |
| - open-source repository docs |
|
|
| ### Date range |
|
|
| - **Search period:** 2010-01-01 to 2026-03-12 |
|
|
| ### Query families |
|
|
| The PubMed search used five overlapping query families: |
|
|
| 1. `pkpd_core` |
| 2. `population_pkpd` |
| 3. `nlme_platforms` |
| 4. `pbpk` |
| 5. `exposure_response_midd` |
|
|
| Per-query unique PMID counts before cross-query deduplication: |
|
|
| | Query family | Unique PMIDs | |
| |---|---:| |
| | `pkpd_core` | 145,533 | |
| | `population_pkpd` | 6,847 | |
| | `nlme_platforms` | 3,932 | |
| | `pbpk` | 4,951 | |
| | `exposure_response_midd` | 12,331 | |
|
|
| After deduplication across query families, the search yielded: |
|
|
| - **156,274 unique PMIDs** |
|
|
| ### Retrieval and filtering stages |
|
|
| Pipeline totals: |
|
|
| 1. PubMed search: **156,274 unique PMIDs** |
| 2. PMID to PMCID mapping: **66,948 PMCIDs** |
| 3. Europe PMC / PMC XML retrieved: **49,097 XML articles** |
| 4. Parsed JATS records: **49,097** |
| 5. Final kept DAPT documents: **27,990** |
|
|
| Retrieval outcomes: |
|
|
| - PMCIDs with XML successfully materialized locally: **49,097** |
| - PMCIDs mapped but not available through Europe PMC fullTextXML: **18,215** |
| - Fetch failures: **0** at the end of the completed run |
|
|
| Filtering outcomes: |
|
|
| - Parsed input docs: **49,097** |
| - Final kept docs: **27,990** |
| - Rejected for low relevance: **19,052** |
| - Rejected for too short length: **2,054** |
| - Rejected as duplicates: **1** |
|
|
| ## Data fields |
|
|
| Each record in the final JSONL contains: |
|
|
| - `id`: document identifier, usually PMCID-based |
| - `source`: source group |
| - `title`: article title |
| - `text`: cleaned training text |
|
|
| Example schema: |
|
|
| ```json |
| { |
| "id": "PMC10010492", |
| "source": "core_pubmed_pmc", |
| "title": "Integrative population pharmacokinetic/pharmacodynamic analysis of nemonoxacin capsule in Chinese patients with community-acquired pneumonia", |
| "text": "..." |
| } |
| ``` |
|
|
| ## Split / repartition |
|
|
| Current files on disk: |
|
|
| - `final_merged_dapt.jsonl`: **27,990** records |
| - `train.jsonl`: **27,431** records |
| - `eval.jsonl`: **559** records |
|
|
| Split proportions: |
|
|
| - **Train:** 27,431 / 27,990 = **98.0%** |
| - **Validation:** 559 / 27,990 = **2.0%** |
|
|
| Source repartition in the final release: |
|
|
| | Source | Documents | Share | |
| |---|---:|---:| |
| | `core_pubmed_pmc` | 27,990 | 100% | |
|
|
| Character / token scale: |
|
|
| - Total characters: **437,602,093** |
| - Average characters per kept document: **15,638.38** |
| - Rough token estimate: **109,400,619** |
|
|
| ## Text extraction details |
|
|
| The XML parser keeps article components most useful for PKPD DAPT: |
|
|
| - title |
| - abstract |
| - methods |
| - modeling |
| - statistical analysis |
| - results |
| - discussion |
| - conclusion |
|
|
| The parser drops low-value or non-training sections when possible: |
|
|
| - references |
| - acknowledgements |
| - funding boilerplate |
| - author contributions |
| - supplementary boilerplate |
|
|
| Whitespace is normalized, and some inline citation clutter is removed. |
|
|
| ## Quality notes |
|
|
| This corpus was built with **high recall rather than high precision**. It is strong for: |
|
|
| - PK/PD language |
| - clinical pharmacology |
| - PBPK |
| - exposure-response |
| - dose optimization |
| - drug disposition and modeling methods |
|
|
| However, the query strategy is broad, and some retained articles are only **adjacent** to pharmacometrics rather than strictly within it. For example, some documents concern: |
|
|
| - broader translational pharmacology |
| - oncology therapeutics |
| - drug-protein binding |
| - formulation or delivery topics |
|
|
| This makes the dataset suitable for a **prototype DAPT corpus**, but not yet a perfectly clean pharmacometrics-only benchmark. |
|
|
| ## Intended uses |
|
|
| Recommended uses: |
|
|
| - domain-adaptive pretraining for LLMs |
| - continued pretraining of Qwen/Llama/Mistral-style causal LMs |
| - corpus mining and keyword analysis |
| - retrieval experiments on PKPD literature |
|
|
| Not recommended as-is for: |
|
|
| - strict pharmacometrics benchmarking without extra curation |
| - legal redistribution assumptions without checking article-level terms |
| - clinical decision support |
|
|
| ## Licensing and redistribution note |
|
|
| This dataset is derived from **PMC / Europe PMC open-access full-text XML** and related PubMed metadata, but the corpus should **not** be interpreted as having a single unified license automatically inherited across all articles. |
|
|
| Important note: |
|
|
| - PMC / Europe PMC accessibility does **not** guarantee identical downstream redistribution terms for every document. |
| - Before making the dataset public, article-level licensing and redistribution conditions should be reviewed carefully. |
|
|
| For conservative use, private hosting is recommended until licensing is fully audited. |
|
|
| ## Reproducibility |
|
|
| The dataset was generated by the local pipeline in: |
|
|
| - `scripts/01_search_pubmed.py` |
| - `scripts/02_map_pmids_to_pmcids.py` |
| - `scripts/03_fetch_fulltext_xml.py` |
| - `scripts/04_parse_jats_xml.py` |
| - `scripts/05_build_dapt_jsonl.py` |
| - `scripts/08_merge_and_report.py` |
|
|
| Summary reports used for this card: |
|
|
| - `data/reports/pubmed_search_summary.json` |
| - `data/reports/fulltext_retrieval_report.json` |
| - `data/reports/parsed_xml_report.json` |
| - `data/reports/core_pubmed_build_report.json` |
| - `data/reports/corpus_summary.json` |
|
|
| ## Loading example |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("Khalilbraham/PKPD-Dataset") |
| print(ds) |
| print(ds["train"][0].keys()) |
| ``` |
|
|
| ## Suggested citation |
|
|
| If you use this dataset, cite: |
|
|
| 1. PubMed / NCBI E-utilities |
| 2. PMC / Europe PMC |
| 3. The dataset repository itself |
|
|
| You may also cite the associated local corpus-building pipeline if released separately. |
|
|
