Datasets:

jang1563
/

NegBioDB

	#!/usr/bin/env python3
	"""Build L2 Abstract Annotation dataset for LLM benchmark.

	Step 1 (automated): Search PubMed for abstracts reporting negative DTI results.
	Step 2 (manual): Human annotation of gold-standard structured extraction.

	This script handles Step 1: abstract retrieval and candidate selection.

	Search strategy:
	- PubMed E-utilities with queries for negative DTI reporting
	- Stratify: 40 explicit / 30 hedged / 30 implicit negative results
	- Output: candidate abstracts for human review

	Output: exports/llm_benchmarks/l2_candidates.jsonl
	(Gold file created manually: l2_gold.jsonl)
	"""

	import argparse
	import json
	import time
	import urllib.request
	import urllib.error
	import xml.etree.ElementTree as ET
	from pathlib import Path

	PROJECT_ROOT = Path(__file__).resolve().parent.parent
	OUTPUT_PATH = PROJECT_ROOT / "exports" / "llm_benchmarks" / "l2_candidates.jsonl"

	PUBMED_BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
	DELAY = 0.4 # seconds between API calls (NCBI recommends max 3/sec without key)


	def search_pubmed(query: str, retmax: int = 100) -> list[str]:
	"""Search PubMed and return list of PMIDs."""
	params = (
	f"db=pubmed&term={urllib.request.quote(query)}"
	f"&retmax={retmax}&retmode=json&sort=relevance"
	)
	url = f"{PUBMED_BASE}/esearch.fcgi?{params}"

	try:
	with urllib.request.urlopen(url, timeout=30) as resp:
	data = json.loads(resp.read())
	return data.get("esearchresult", {}).get("idlist", [])
	except Exception as e:
	print(f" Search error: {e}")
	return []


	def fetch_abstracts(pmids: list[str]) -> list[dict]:
	"""Fetch abstract text for list of PMIDs using efetch."""
	if not pmids:
	return []

	# Batch fetch (up to 200 per request)
	results = []
	for i in range(0, len(pmids), 100):
	batch = pmids[i : i + 100]
	ids = ",".join(batch)
	url = f"{PUBMED_BASE}/efetch.fcgi?db=pubmed&id={ids}&rettype=xml"

	try:
	with urllib.request.urlopen(url, timeout=60) as resp:
	xml_text = resp.read()
	except Exception as e:
	print(f" Fetch error for batch {i//100}: {e}")
	continue

	root = ET.fromstring(xml_text)
	for article in root.findall(".//PubmedArticle"):
	pmid_el = article.find(".//PMID")
	title_el = article.find(".//ArticleTitle")
	abstract_el = article.find(".//Abstract")

	if pmid_el is None or abstract_el is None:
	continue

	pmid = pmid_el.text
	title = title_el.text if title_el is not None else ""

	# Concatenate all AbstractText elements
	abstract_parts = []
	for at in abstract_el.findall("AbstractText"):
	label = at.get("Label", "")
	text = "".join(at.itertext()).strip()
	if label:
	abstract_parts.append(f"{label}: {text}")
	else:
	abstract_parts.append(text)
	abstract_text = " ".join(abstract_parts)

	# Get year
	year_el = article.find(".//PubDate/Year")
	year = int(year_el.text) if year_el is not None else None

	if abstract_text and len(abstract_text) > 100:
	results.append(
	{
	"pmid": pmid,
	"title": title,
	"abstract_text": abstract_text,
	"year": year,
	}
	)

	time.sleep(DELAY)

	return results


	# Negative DTI reporting search queries by category
	QUERIES = {
	"explicit": [
	# Explicit statements of inactivity
	'("did not inhibit" OR "no inhibition" OR "showed no activity") AND '
	'("drug target" OR "IC50" OR "binding assay") AND '
	'("selectivity" OR "specificity") AND 2020:2025[dp]',
	# HTS negative results
	'("inactive" OR "no effect") AND ("high-throughput screening" OR "HTS") '
	'AND ("kinase" OR "protease" OR "GPCR") AND 2018:2025[dp]',
	],
	"hedged": [
	# Hedged/qualified negative results
	'("weak activity" OR "marginal" OR "modest inhibition") AND '
	'("IC50" OR "Ki" OR "Kd") AND ("selectivity panel" OR "kinase panel") '
	'AND 2019:2025[dp]',
	# Borderline results
	'("borderline" OR "insufficient" OR "below threshold") AND '
	'("drug discovery" OR "medicinal chemistry") AND '
	'("IC50 >" OR "Ki >") AND 2018:2025[dp]',
	],
	"implicit": [
	# Implicit negatives (selectivity studies where some targets are inactive)
	'("selectivity profile" OR "kinome scan" OR "selectivity panel") AND '
	'("selective for" OR "selective inhibitor") AND '
	'("drug target interaction" OR "kinase inhibitor") AND 2019:2025[dp]',
	# SAR studies with inactive analogues
	'("structure-activity relationship" OR "SAR") AND '
	'("inactive analogue" OR "loss of activity" OR "no binding") '
	'AND 2018:2025[dp]',
	],
	}


	def main():
	parser = argparse.ArgumentParser(description="Search PubMed for L2 candidates")
	parser.add_argument(
	"--per-query", type=int, default=80, help="Max PMIDs per query"
	)
	args = parser.parse_args()

	all_abstracts = {} # pmid -> record (dedup)

	for category, queries in QUERIES.items():
	print(f"\n=== Category: {category} ===")
	for q in queries:
	print(f" Query: {q[:80]}...")
	pmids = search_pubmed(q, retmax=args.per_query)
	print(f" Found: {len(pmids)} PMIDs")

	if pmids:
	abstracts = fetch_abstracts(pmids)
	for a in abstracts:
	a["search_category"] = category
	all_abstracts[a["pmid"]] = a
	print(f" Fetched: {len(abstracts)} abstracts with text")

	time.sleep(DELAY)

	# Stratify: target 40 explicit / 30 hedged / 30 implicit
	by_cat = {}
	for rec in all_abstracts.values():
	by_cat.setdefault(rec["search_category"], []).append(rec)

	targets = {"explicit": 50, "hedged": 40, "implicit": 40}
	selected = []
	for cat, recs in by_cat.items():
	n = targets.get(cat, 30)
	selected.extend(recs[:n])

	print(f"\n=== Summary ===")
	print(f"Total unique abstracts: {len(all_abstracts)}")
	from collections import Counter

	cat_counts = Counter(r["search_category"] for r in selected)
	print(f"Selected: {len(selected)} ({dict(cat_counts)})")

	# Save candidates
	OUTPUT_PATH.parent.mkdir(parents=True, exist_ok=True)
	with open(OUTPUT_PATH, "w") as f:
	for i, rec in enumerate(selected):
	rec["candidate_id"] = f"L2-C{i:04d}"
	f.write(json.dumps(rec, ensure_ascii=False) + "\n")

	print(f"\nSaved to {OUTPUT_PATH}")
	print(f"\nNext step: Human review + annotation → l2_gold.jsonl")


	if __name__ == "__main__":
	main()