Checking ChEMBL for Structured SAR Data

Overview

ChEMBL is a manually curated database of ~99,000 medicinal chemistry papers with extracted, standardized bioactivity data. If a paper is in ChEMBL, you can access structured data without parsing PDFs.

Core principle: Check ChEMBL first for medicinal chemistry papers. Curated data is more reliable than table parsing.

When to Use

Use this skill when:

Paper describes medicinal chemistry / drug discovery
Abstract mentions compound series, SAR, or activity data
Paper has IC50, MIC, Ki, EC50, or other bioactivity measurements
Before attempting to extract data from tables/figures
Paper scored ≥ 7 in relevance evaluation

When NOT to use:

Non-medicinal chemistry papers (cell biology, genomics, etc.)
Papers without activity measurements
Reviews without primary data
Very recent papers (< 6 months, likely not curated yet)

ChEMBL API Basics

Base URL: https://www.ebi.ac.uk/chembl/api/data/

No authentication required

CRITICAL: ChEMBL can ONLY be queried by DOI, NOT by PMID

The API returns PMID in results, but does not accept it as a query parameter
Always use DOI for lookups: ?doi=10.1234/example
PMID queries will return 0 results even if paper exists in ChEMBL

Two-step process:

Check if paper (by DOI) is in ChEMBL
If yes, retrieve bioactivity data

Step 1: Check if Paper in ChEMBL

Query by DOI (ONLY method that works):

curl -s "https://www.ebi.ac.uk/chembl/api/data/document.json?doi=DOI"

⚠️ IMPORTANT: Must use DOI, not PMID

# ✅ CORRECT - Use DOI
doi="10.1021/jm401507s"
curl -s "https://www.ebi.ac.uk/chembl/api/data/document.json?doi=$doi"

# ❌ WRONG - PMID won't work (will return 0 results)
pmid="24446688"
curl -s "https://www.ebi.ac.uk/chembl/api/data/document.json?pubmed_id=$pmid"  # Does NOT work!

If you only have PMID: Fetch DOI from PubMed first, then query ChEMBL with the DOI.

Response structure:

{
  "documents": [
    {
      "document_chembl_id": "CHEMBL3120156",
      "doi": "10.1021/jm401507s",
      "title": "Discovery and development of simeprevir (TMC435), a HCV NS3/4A protease inhibitor.",
      "abstract": "Hepatitis C virus is a blood-borne infection...",
      "pubmed_id": 24446688,
      "journal": "J Med Chem",
      "year": 2014,
      "doc_type": "PUBLICATION"
    }
  ],
  "page_meta": {
    "total_count": 1
  }
}

Key fields:

document_chembl_id - Use this to retrieve activity data
doc_type - "PUBLICATION" (from literature) or "DATASET" (deposited)
pubmed_id - PMID is in the response, but cannot be used to query ChEMBL
If total_count = 0, paper not in ChEMBL

Parse response:

response=$(curl -s "https://www.ebi.ac.uk/chembl/api/data/document.json?doi=$doi")

if [ $(echo "$response" | jq -r '.page_meta.total_count') -gt 0 ]; then
  chembl_id=$(echo "$response" | jq -r '.documents[0].document_chembl_id')
  echo "✓ Found in ChEMBL: $chembl_id"
else
  echo "✗ Not in ChEMBL"
fi

Step 2: Get Activity Data Count

Query activity endpoint:

curl -s "https://www.ebi.ac.uk/chembl/api/data/activity.json?document_chembl_id=CHEMBL3120156&limit=1"

Extract total count:

activity_url="https://www.ebi.ac.uk/chembl/api/data/activity.json?document_chembl_id=$chembl_id&limit=1"
activity_count=$(curl -s "$activity_url" | jq -r '.page_meta.total_count')

echo "→ $activity_count bioactivity data points"

Step 3: Report to User and Update Summary

Report immediately:

📄 [15/127] Screening: "Discovery and development of simeprevir"
   Abstract score: 9 → Fetching full text...
   ✓ ChEMBL: CHEMBL3120156 (101 activity data points)
   → IC50 data for HCV NS3 protease inhibitors available

Add to SUMMARY.md:

### [Discovery and development of simeprevir (TMC435), a HCV NS3/4A protease inhibitor](https://doi.org/10.1021/jm401507s) (Score: 9)

**DOI:** [10.1021/jm401507s](https://doi.org/10.1021/jm401507s)
**PMID:** [24446688](https://pubmed.ncbi.nlm.nih.gov/24446688/)
**ChEMBL:** [CHEMBL3120156](https://www.ebi.ac.uk/chembl/document_report_card/CHEMBL3120156/) (101 data points)

**Key Findings:**
- IC50 data for HCV NS3/4A protease inhibitors (from ChEMBL)
- Lead compound simeprevir (TMC435) approved for HCV treatment
- Structures and full activity data: [ChEMBL API](https://www.ebi.ac.uk/chembl/api/data/activity.json?document_chembl_id=CHEMBL3120156)

**ChEMBL Activity Summary:**
- IC50 values for HCV NS3/4A protease
- PK parameters (AUC, Cmax, clearance)
- DMPK assays (metabolic stability, permeability)

Always include ChEMBL status:

If found: Add ChEMBL ID with link and data point count
If not found: Note "Not in ChEMBL" (still valuable information)

Step 4: Update Tracking Files

Add to papers-reviewed.json:

{
  "10.1021/jm401507s": {
    "pmid": "24446688",
    "status": "relevant",
    "score": 9,
    "chembl_id": "CHEMBL3120156",
    "chembl_activities": 101,
    "has_structured_data": true
  }
}

Optional: Extract Structured Data

For papers with rich ChEMBL data (>20 activities), consider extracting:

# Get all IC50 data
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity.json?document_chembl_id=CHEMBL3120156&standard_type=IC50&limit=100" > chembl_data.json

# Summary statistics
jq '[.activities[] | .standard_value | tonumber] | "Min: \(min), Max: \(max), Count: \(length)"' chembl_data.json

Report to user:

📊 ChEMBL data extracted:
   - IC50 values for HCV NS3/4A protease
   - All structures downloaded
   - Data saved to: chembl_CHEMBL3120156_ic50.json

Integration with Other Skills

During evaluating-paper-relevance workflow:

After abstract screening (score ≥7)
Before deep dive into full text
Check ChEMBL using this skill
If found:
- Note ChEMBL ID in SUMMARY.md
- Extract activity data (faster than PDF parsing)
- Still fetch full text for methods, discussion, context
If not found:
- Proceed with normal PDF evaluation
- Parse tables manually if needed

Workflow integration point:

Stage 2: Deep Dive
├─ 1. Fetch Full Text (PMC → DOI → Unpaywall)
├─ 1.5. Check ChEMBL ← ADD THIS STEP
│   ├─ Query by DOI
│   ├─ If found: note ChEMBL ID + activity count
│   └─ Report to user
├─ 2. Scan for Relevant Content
└─ 3. Extract Findings

Common Activity Types in ChEMBL

Type	Description	Units
IC50	Half-maximal inhibitory concentration	nM, µM
MIC	Minimum inhibitory concentration	µg/mL, nM
Ki	Inhibition constant	nM, µM
EC50	Half-maximal effective concentration	nM, µM
Kd	Dissociation constant	nM, µM
Potency	General potency measurement	Various

Filter by activity type:

curl "https://www.ebi.ac.uk/chembl/api/data/activity.json?document_chembl_id=ID&standard_type=MIC"

ChEMBL Coverage

~99,000 documents (as of 2025)

Well represented:

Medicinal chemistry papers
SAR studies with compound series
Lead optimization campaigns
Papers in major journals (J Med Chem, Bioorg Med Chem, Eur J Med Chem, etc.)

Poorly represented:

Very recent papers (6-12 month curation lag)
Papers without extractable structures/activities
Non-drug-discovery research
Purely mechanistic studies

Typical hit rate:

~30-40% of medicinal chemistry papers
Higher for SAR-focused journals

Advantages of ChEMBL Data

vs. PDF table parsing:

✓ Structures already extracted (SMILES format)
✓ Units standardized (all IC50s in nM)
✓ Values validated and curated
✓ Machine-readable JSON
✓ No OCR errors
✓ Linked to assay protocols
✓ Queryable (filter by activity range, target, etc.)

When to still use PDF:

Full experimental procedures
Synthesis routes
Papers not in ChEMBL
Very recent papers
Context and interpretation

Progress Reporting

CRITICAL: Report ChEMBL check for every relevant paper

Example workflow report:

📄 [15/50] Screening: "Novel MmpL3 inhibitors..."
   Abstract score: 8 → Checking ChEMBL...
   ✓ ChEMBL: CHEMBL3456789 (34 data points)
   → Fetching full text...
   → Added to SUMMARY.md with ChEMBL link

For papers not in ChEMBL:

📄 [16/50] Screening: "Another paper..."
   Abstract score: 9 → Checking ChEMBL...
   ✗ Not in ChEMBL (likely too recent or review paper)
   → Fetching full text via Unpaywall...

Helper Script Pattern

For research sessions with many medicinal chemistry papers:

Create check_chembl.py:

#!/usr/bin/env python3
import requests
import json
import sys

def check_chembl(doi):
    """Check if DOI is in ChEMBL and return summary

    IMPORTANT: Must use DOI, not PMID. ChEMBL API does not accept PMID queries.
    """

    # Query document (ONLY works with DOI)
    doc_url = f"https://www.ebi.ac.uk/chembl/api/data/document.json?doi={doi}"
    try:
        doc_response = requests.get(doc_url, timeout=10).json()
    except:
        return None

    # Check if found
    if doc_response.get('page_meta', {}).get('total_count', 0) == 0:
        return {'in_chembl': False}

    doc = doc_response['documents'][0]
    chembl_id = doc['document_chembl_id']

    # Get activity count
    act_url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?document_chembl_id={chembl_id}&limit=1"
    try:
        act_response = requests.get(act_url, timeout=10).json()
        activity_count = act_response.get('page_meta', {}).get('total_count', 0)
    except:
        activity_count = 0

    return {
        'in_chembl': True,
        'chembl_id': chembl_id,
        'activity_count': activity_count,
        'doc_type': doc.get('doc_type'),
        'title': doc.get('title')
    }

if __name__ == "__main__":
    doi = sys.argv[1]
    result = check_chembl(doi)

    if result and result['in_chembl']:
        print(f"✓ {result['chembl_id']} ({result['activity_count']} activities)")
    else:
        print("✗ Not in ChEMBL")

Usage:

python3 check_chembl.py "10.1021/jm401507s"
# Output: ✓ CHEMBL3120156 (101 activities)

Common Mistakes

Querying by PMID: Using PMID instead of DOI → Always returns 0 results, ChEMBL only accepts DOI queries Skipping ChEMBL check: Not checking medicinal chemistry papers → Missing structured data that's already extracted Checking non-medchem papers: Checking genomics/cell biology papers → Wasting time, won't be in ChEMBL Not reporting status: Silent ChEMBL checks → User can't see what's happening Not adding to SUMMARY.md: Forgetting to include ChEMBL ID → Harder for user to access data later Only using ChEMBL: Not fetching full text when paper in ChEMBL → Missing context, methods, discussion Parsing PDFs when in ChEMBL: Manually extracting tables when structured data available → Wasting time and introducing errors

Quick Reference

Task	Command
Check if DOI in ChEMBL	`curl "https://www.ebi.ac.uk/chembl/api/data/document.json?doi=DOI"`
Get activity count	`curl "https://www.ebi.ac.uk/chembl/api/data/activity.json?document_chembl_id=ID&limit=1"`
Get all activities	`curl "https://www.ebi.ac.uk/chembl/api/data/activity.json?document_chembl_id=ID&limit=1000"`
Filter by activity type	`curl "...activity.json?document_chembl_id=ID&standard_type=MIC"`
ChEMBL paper page	`https://www.ebi.ac.uk/chembl/document_report_card/CHEMBL_ID/`

Permissions

Add to .claude/settings.local.json.template:

"Bash(curl*https://www.ebi.ac.uk/chembl/api/data/*)",
"WebFetch(domain:www.ebi.ac.uk)"

Success Criteria

ChEMBL check successful when:

Every medicinal chemistry paper (score ≥7) checked
ChEMBL status reported to user immediately
ChEMBL ID added to SUMMARY.md (if found)
Activity count noted in summary
papers-reviewed.json updated with ChEMBL status

Next Steps

After checking ChEMBL:

If found: Consider extracting structured data for highly relevant papers (≥9)
Continue with full text evaluation for context
For papers not in ChEMBL: Proceed with normal PDF/table parsing
Update SUMMARY.md with all findings

Resources

Full Documentation: See docs/CHEMBL_INTEGRATION.md
ChEMBL API Docs: https://chembl.gitbook.io/chembl-interface-documentation/
ChEMBL Interface: https://www.ebi.ac.uk/chembl/

Checking ChEMBL for Structured SAR Data

Checking ChEMBL for Structured SAR Data

Overview

When to Use

ChEMBL API Basics

Step 1: Check if Paper in ChEMBL

Step 2: Get Activity Data Count

Step 3: Report to User and Update Summary

Step 4: Update Tracking Files

Optional: Extract Structured Data

Integration with Other Skills

Common Activity Types in ChEMBL

ChEMBL Coverage

Advantages of ChEMBL Data

Progress Reporting

Helper Script Pattern

Common Mistakes

Quick Reference

Permissions

Success Criteria

Next Steps

Resources

Similar Skills