Khalilbraham/PKPD-Dataset

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:

{
  "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

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.