Pubmed Search

PubMed Search

Search NCBI PubMed for scientific literature using BioPython's Entrez module.

When to Use

• User asks to find papers on a topic
• User wants recent publications in a field
• User asks for references or citations
• User wants to know the state of research on a topic

How to Execute

1. Set up Entrez

from Bio import Entrez
Entrez.email = "medclaw@freedomai.com"

2. Search PubMed

# Search
handle = Entrez.esearch(db="pubmed", term="CRISPR delivery methods", retmax=20, sort="date")
record = Entrez.read(handle)
handle.close()

id_list = record["IdList"]
print(f"Found {record['Count']} results, showing top {len(id_list)}")

3. Fetch article details

# Fetch details
handle = Entrez.efetch(db="pubmed", id=id_list, rettype="xml")
records = Entrez.read(handle)
handle.close()

for article in records['PubmedArticle']:
    medline = article['MedlineCitation']
    pmid = str(medline['PMID'])
    title = medline['Article']['ArticleTitle']
    
    # Get authors
    authors = medline['Article'].get('AuthorList', [])
    first_author = f"{authors[0].get('LastName', '')} {authors[0].get('Initials', '')}" if authors else "Unknown"
    
    # Get journal and year
    journal = medline['Article']['Journal']['Title']
    pub_date = medline['Article']['Journal']['JournalIssue'].get('PubDate', {})
    year = pub_date.get('Year', 'N/A')
    
    # Get abstract
    abstract_parts = medline['Article'].get('Abstract', {}).get('AbstractText', [])
    abstract = ' '.join(str(a) for a in abstract_parts)[:300]
    
    print(f"PMID: {pmid}")
    print(f"Title: {title}")
    print(f"Authors: {first_author} et al.")
    print(f"Journal: {journal} ({year})")
    print(f"Abstract: {abstract}...")
    print(f"Link: https://pubmed.ncbi.nlm.nih.gov/{pmid}/")
    print()

4. Drug Discovery Query Templates

Common search patterns for computational drug discovery tasks:

# Target validation / background
term = '"[TARGET]"[Title] AND (review[Publication Type] OR "drug target"[Title/Abstract])'

# Known inhibitors / binders with binding data
term = '"[TARGET]" AND (inhibitor OR antagonist) AND (IC50 OR Ki OR Kd)[Title/Abstract]'

# Crystal structures with ligands
term = '"[TARGET]" AND "crystal structure"[Title] AND "ligand"[Title/Abstract]'

# Virtual screening / computational docking studies
term = '"[TARGET]" AND ("molecular docking" OR "virtual screening")[Title/Abstract]'

# SAR studies
term = '"[TARGET]" AND "structure-activity relationship"[Title/Abstract]'

# Binding free energy / MMPBSA benchmarks
term = '"[TARGET]" AND ("binding free energy" OR "MM-PBSA" OR "MM-GBSA")[Title/Abstract]'

# Peptide / protein-protein interaction
term = '"[TARGET]" AND ("protein-protein interaction" OR "peptide inhibitor")[Title/Abstract]'

# ADMET / pharmacokinetics for compound class
term = '"[COMPOUND CLASS]" AND (ADMET OR pharmacokinetics OR "drug-likeness")[Title/Abstract]'

MolClaw integration requirements:

• Every retrieved result MUST be labeled as Category 3 information (⚠️ LITERATURE VALUE) per Principle 10.
• Output MUST include PMID, DOI (when available), first author, year, journal for each citation.
• Retrieved literature values NEVER substitute for computational results (Principle 13).
• Save search results as stepNN_LR_pubmed_[topic].md following MolClaw file naming convention.

5. Advanced searches

Support these query patterns:

• "CRISPR"[Title] AND "delivery"[Title] — title-specific
• "2026"[Date - Publication] — date filter
• "Nature"[Journal] — journal filter
• review[Publication Type] — type filter

6. Follow-up suggestions

After showing results, suggest:

• "Want me to summarize any of these papers?"
• "Should I search with different keywords?"
• "Want me to find related papers to any of these?"