clinpipe 0.6.3

A research-grade pipeline linking PubChem compounds with ClinicalTrials.gov study data

clinical-data-pipeline

A research-grade pipeline for collecting, normalizing, and linking clinical compound data from PubChem with clinical trial documents from ClinicalTrials.gov.

Korean README

• GitHub repository
• Documentation
• Issue tracker

This repository focuses on reproducible, API-based data collection. It prioritizes official APIs and uses web-derived fallbacks only when PubChem REST payloads do not expose trial IDs for specific compounds.

This package is intended for research data collection and analysis. It does not provide medical advice, clinical recommendations, or regulatory conclusions.

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Beginner Quickstart (Copy & Paste)

If this is your first run, use this section first.

Option A: Conda (recommended if you already use conda)

conda create -n clinical-pipeline python=3.11 -y
conda activate clinical-pipeline
pip install clinpipe

Run a small smoke test (first CID + first NCT):

clinpipe collect-ctgov \
  --hnid 1856916 \
  --limit 1 \
  --out out_ctgov_smoke

Option B: uv (recommended if you prefer fast Python tooling)

uv venv .venv --python 3.11
source .venv/bin/activate
uv pip install clinpipe

Run the same smoke test:

clinpipe collect-ctgov \
  --hnid 1856916 \
  --limit 1 \
  --out out_ctgov_smoke

Expected output files:

out_ctgov_smoke/
├─ cid_nct_links.jsonl
├─ compounds.jsonl
└─ studies.jsonl

---

What this project does

This project provides a minimal but extensible pipeline to:

1. Retrieve clinical-trial–related compounds from PubChem

   • Uses the official PubChem PUG REST Classification Nodes API
   • Retrieves compound lists (CIDs) from specific classification nodes (HNIDs), such as Clinical Trials and ClinicalTrials.gov

2. Collect compound metadata from PubChem

   • Canonical SMILES, InChIKey, IUPAC name
   • Synonyms and identifiers via PUG REST

3. Link PubChem compounds to ClinicalTrials.gov

   • Extracts NCT IDs from PubChem annotations (PUG-View)
   • Uses fallback sources when needed (PUG-View heading lookup, PubChem web clinicaltrials endpoint, optional CT.gov term linking)
   • Retrieves full clinical trial documents via the ClinicalTrials.gov v2 API

4. Export analysis-ready datasets

   • JSONL outputs for compounds, links, and clinical trial documents
   • Designed to be consumed by downstream analysis, modeling, or visualization pipelines

---

Key design principles

• Official APIs first

  • PubChem PUG REST (Classification Nodes, PUG-View)
  • ClinicalTrials.gov v2 API
  • PubChem web clinicaltrials endpoint fallback (/sdq/sphinxql.cgi) when REST payload is incomplete

• No Selenium/browser automation

• Reproducible

  • Classification nodes (HNID) are stable identifiers

• Modular

  • PubChem-related functionality is organized as a self-contained subpackage

---

PubChem classification nodes (HNID)

PubChem provides an official API to retrieve identifiers associated with classification nodes:

https://pubchem.ncbi.nlm.nih.gov/rest/pug/classification/hnid/{HNID}/{id_type}/{format}

This project currently supports compound (CID) retrieval from clinical-trial–related nodes.

Clinical trial–related HNIDs used

HNID	Description
1856916	Clinical Trials (all sources)
3647573	ClinicalTrials.gov
3647574	EU Clinical Trials Register
3647575	NIPH Clinical Trials Search of Japan

---

Quick examples

1) Download PubChem CIDs for clinical trials (HNID-based)

from clinpipe.pubchem.clinical_trials_nodes import download_clinical_trials_cids

results = download_clinical_trials_cids(out_dir="out_hnid")

print("Clinical Trials (all):", len(results["clinical_trials"]))
print("ClinicalTrials.gov only:", len(results["clinicaltrials_gov"]))

This will create files such as:

out_hnid/
├─ clinical_trials_cids.txt
├─ clinicaltrials_gov_cids.txt
├─ eu_register_cids.txt
└─ japan_niph_cids.txt

---

2) From HNID → CID → ClinicalTrials.gov documents

The example below shows the full pipeline:

1. download clinical-trial–related CIDs from PubChem (HNID)
2. extract NCT IDs from PubChem annotations (PUG-View)
3. retrieve full trial documents from ClinicalTrials.gov

from clinpipe.pubchem import (
    PubChemClient,
    PubChemClassificationClient,
    PubChemPugViewClient,
)
from clinpipe.ctgov import CTGovClient

# Clinical Trials HNID
HNID = 1856916

pubchem = PubChemClient()
class_nodes = PubChemClassificationClient()
pug_view = PubChemPugViewClient()
ctgov = CTGovClient()

# Step 1: HNID → CID list
cids = class_nodes.get_cids(HNID)
print("Total CIDs:", len(cids))

# (optional) limit for a quick test
cids = cids[:10]

# Step 2–3: CID → NCT → CTGov study document
for cid in cids:
    nct_ids = pug_view.nct_ids_for_cid(cid)
    for nct in nct_ids:
        study = ctgov.get_study(nct)
        print(cid, nct, study.get("protocolSection", {}).get("identificationModule", {}).get("briefTitle"))

This example demonstrates how the individual modules can be composed into a reproducible, end-to-end data collection pipeline.

This will create files such as:

out_hnid/
├─ clinical_trials_cids.txt
├─ clinicaltrials_gov_cids.txt
├─ eu_register_cids.txt
└─ japan_niph_cids.txt

---

Package structure

src/clinpipe/
├─ pubchem/
│  ├─ client.py                  # PUG REST: CID, properties, synonyms
│  ├─ classification_nodes.py    # HNID → CID (Classification Nodes API)
│  ├─ clinical_trials_nodes.py   # Clinical-trial–related HNID helpers
│  └─ pug_view.py                # PUG-View: NCT ID extraction
│  └─ web_fallback/              # Web clinicaltrials endpoint/HTML fallback for NCT IDs
│
├─ ctgov/
│  └─ client.py                  # ClinicalTrials.gov v2 API
│
├─ pipeline/
│  └─ ...                        # Dataset builders and linkers

---

Installation

User Install (PyPI)

pip install clinpipe

or with uv:

uv pip install clinpipe

Quick smoke:

clinpipe collect-ctgov \
  --hnid 1856916 \
  --limit 1 \
  --out out_ctgov_smoke

Expected output:

out_ctgov_smoke/
├─ cid_nct_links.jsonl
├─ compounds.jsonl
└─ studies.jsonl

Development Setup (Repository)

Choose one setup method.

uv (Recommended for fast local setup)

Create environment and install:

uv venv .venv --python 3.11
source .venv/bin/activate
uv pip install -e .

Optional development dependencies:

uv pip install -e ".[dev]"

Run without activating shell state (optional):

uv run python scripts/run_mvp_pipeline.py --hnid 3647573 --out-dir out_mvp --use-ctgov-fallback --resume

Conda (Recommended if you already use conda)

conda create -n clinical-pipeline python=3.11 -y
conda activate clinical-pipeline
pip install -e .

Optional development dependencies:

pip install -e ".[dev]"

venv (Alternative)

python -m venv .venv
source .venv/bin/activate
pip install -e .

Optional development dependencies:

pip install -e ".[dev]"

---

Documentation

Project documentation is in the docs/ folder.

• English:
  • docs/overview.md
  • docs/ctgov.md
  • docs/pubchem.md
  • docs/pipeline.md
  • docs/cli.md
• Korean:
  • docs/overview.ko.md
  • docs/ctgov.ko.md
  • docs/pubchem.ko.md
  • docs/pipeline.ko.md
  • docs/cli.ko.md

---

CLI usage

A minimal command-line interface is provided for quick, reproducible runs without writing Python code.

Show help

clinpipe --help

Legacy alias (still supported): clinical-data-analyzer Import alias (transition path): clinpipe (while clinical_data_analyzer remains supported)

Script usage (MVP)

For staged execution:

python scripts/fetch_cids.py --hnid 3647573 --out-dir out_mvp
python scripts/map_cid_to_nct.py --cids-file out_mvp/cids.txt --out-dir out_mvp --use-ctgov-fallback
python scripts/fetch_ctgov_docs.py --links-file out_mvp/cid_nct_links.jsonl --out-path out_mvp/studies.jsonl --resume
python scripts/build_clinical_dataset.py --links-file out_mvp/cid_nct_links.jsonl --studies-file out_mvp/studies.jsonl --out-dir out_mvp/final

One-shot:

python scripts/run_mvp_pipeline.py --hnid 3647573 --out-dir out_mvp --use-ctgov-fallback --resume

Step1-3 only (streaming CID -> NCT -> CTGov docs, progress-friendly):

PYTHONUNBUFFERED=1 conda run -n clinical-pipeline python -u scripts/collect_ctgov_docs.py \
  --hnid 3647573 \
  --folder-name ctgov_docs_run1 \
  --out-root out \
  --use-ctgov-fallback \
  --resume \
  --show-progress \
  --progress-every 1

Quick smoke (first CID + first NCT):

PYTHONUNBUFFERED=1 conda run -n clinical-pipeline python -u scripts/collect_ctgov_docs.py \
  --hnid 3647573 \
  --limit-cids 1 \
  --limit-ncts 1 \
  --folder-name ctgov_docs_first1 \
  --out-root out \
  --use-ctgov-fallback \
  --show-progress \
  --progress-every 1

Scheduled Automation (GitHub Actions)

The repository includes a scheduled workflow:

• .github/workflows/ctgov_collect.yml
• .github/workflows/clinical_compound_table_pages.yml

What it does on each run:

1. collect/refresh CTGov docs (collect_ctgov_docs.py --resume)
2. build normalized dataset (build_clinical_dataset.py)
3. build static table page (build_studies_table.py)
4. update persistent data snapshots in repo:
   • data/ctgov/studies.jsonl (latest)
   • data/ctgov/history/studies_*.jsonl (history, only when changed)
   • data/ctgov/collection_state.json (last collected/changed metadata)
5. deploy table page from docs/data to GitHub Pages

Manual run (Actions UI) supports optional:

• hnid
• limit_cids
• limit_ncts
• shard_size (500 default, 0 disables shard mode)

Recommended workflow presets:

• smoke check:
  • limit_cids=200
  • shard_size=200
  • image_size=400x400
• production run:
  • limit_cids= (empty)
  • shard_size=500 (start here; tune by runtime)
  • image_size=400x400

PubChem workflow snapshot outputs:

• snapshots/clinical_trials/latest/trials.json (latest)
• snapshots/clinical_trials/latest/compounds.json (CID-level compound cache)
• snapshots/clinical_trials/latest/trials_compact.json (trial-only compact rows)
• snapshots/clinical_trials/history/trials_*.json (timestamped history)
• snapshots/clinical_trials/history/compounds_*.json (timestamped compound history)
• snapshots/clinical_trials/history/trials_compact_*.json (timestamped compact history)
• snapshots/clinical_trials/collection_state.json (last collected/changed metadata, includes source: pubchem)

Local snapshot update after collecting dataset files:

python scripts/update_pubchem_trials_history.py \
  --trials-file out/pubchem_trials_dataset_check_v2/trials.json \
  --compounds-file out/pubchem_trials_dataset_check_v2/compounds.json \
  --trials-compact-file out/pubchem_trials_dataset_check_v2/trials_compact.json \
  --state-file snapshots/clinical_trials/collection_state.json \
  --latest-file snapshots/clinical_trials/latest/trials.json \
  --latest-compounds-file snapshots/clinical_trials/latest/compounds.json \
  --latest-trials-compact-file snapshots/clinical_trials/latest/trials_compact.json \
  --history-dir snapshots/clinical_trials/history \
  --retention-days 365

Shard collection and merge (recommended for large runs):

# shard 1 (first 500 CIDs)
python scripts/export_pubchem_trials_dataset.py \
  --hnid 1856916 \
  --cid-offset 0 \
  --cid-count 500 \
  --resume \
  --out-dir out/pubchem_trials_shards/s1

# shard 2 (next 500 CIDs)
python scripts/export_pubchem_trials_dataset.py \
  --hnid 1856916 \
  --cid-offset 500 \
  --cid-count 500 \
  --resume \
  --out-dir out/pubchem_trials_shards/s2

# merge shards
python scripts/merge_pubchem_trials_shards.py \
  --shard-dirs out/pubchem_trials_shards/s1,out/pubchem_trials_shards/s2 \
  --out-dir out/pubchem_trials_merged

Package CI and Publishing

• CI workflow: .github/workflows/package_ci.yml
  • runs focused tests
  • builds package (python -m build)
  • validates artifacts (twine check)
• Publish workflow: .github/workflows/publish_pypi.yml (manual dispatch)
  • repository=testpypi uses TEST_PYPI_API_TOKEN
  • repository=pypi uses PYPI_API_TOKEN

TestPyPI install smoke command:

python -m pip install \
  --index-url https://test.pypi.org/simple/ \
  --extra-index-url https://pypi.org/simple \
  clinpipe

Clinical Compound Table Pages (Tabulator Default)

The repository also includes a dedicated Pages workflow for the latest PubChem clinical trial snapshot:

• .github/workflows/clinical_compound_table_pages.yml

What it does on each run:

1. read the latest snapshot files from snapshots/clinical_trials/latest/
2. build static HTML table (scripts/build_pubchem_trials_table.py)
3. publish index.html, trials.json, trials.csv, summary.json, cids.txt to GitHub Pages

Manual run (Actions UI) input:

• table_mode (default: tabulator, also supports vanilla, datatables)

Download clinical-trial–related CIDs (HNID)

Download PubChem compound IDs associated with the Clinical Trials classification node:

clinpipe hnid-cids \
  --hnid 1856916 \
  --out out_hnid/clinical_trials_cids.txt

This uses the official PubChem Classification Nodes API:

/rest/pug/classification/hnid/{HNID}/cids/TXT

End-to-end example: HNID → CID → ClinicalTrials.gov documents

Run a small end-to-end collection for a quick sanity check:

clinpipe collect-ctgov \
  --hnid 1856916 \
  --limit 10 \
  --out out_ctgov

This will:

1. retrieve CIDs from the given HNID
2. extract NCT IDs from PubChem annotations (PUG-View)
3. download full trial documents from ClinicalTrials.gov

Generated files:

out_ctgov/
├─ compounds.jsonl
├─ links.jsonl
└─ studies.jsonl

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Contributors

• Young-Mook Kang (Korea Research Institute of Chemical Technology, KRICT; National AI for Science Research Center, NAIS)

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License

MIT License