#!/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()