PubChem Database

Overview

PubChem is the world's largest freely available chemical database with 110M+ compounds and 270M+ bioactivities. Query chemical structures by name, CID, or SMILES, retrieve molecular properties, perform similarity and substructure searches, access bioactivity data using PUG-REST API and PubChemPy.

When to Use This Skill

This skill should be used when:

• Searching for chemical compounds by name, structure (SMILES/InChI), or molecular formula

• Retrieving molecular properties (MW, LogP, TPSA, hydrogen bonding descriptors)

• Performing similarity searches to find structurally related compounds

• Conducting substructure searches for specific chemical motifs

• Accessing bioactivity data from screening assays

• Converting between chemical identifier formats (CID, SMILES, InChI)

• Batch processing multiple compounds for drug-likeness screening or property analysis

Core Capabilities

1. Chemical Structure Search

Search for compounds using multiple identifier types:

By Chemical Name:

import pubchempy as pcp compounds = pcp.get_compounds('aspirin', 'name') compound = compounds[0]

By CID (Compound ID):

compound = pcp.Compound.from_cid(2244) # Aspirin

By SMILES:

compound = pcp.get_compounds('CC(=O)OC1=CC=CC=C1C(=O)O', 'smiles')[0]

By InChI:

compound = pcp.get_compounds('InChI=1S/C9H8O4/...', 'inchi')[0]

By Molecular Formula:

compounds = pcp.get_compounds('C9H8O4', 'formula')

Returns all compounds matching this formula

2. Property Retrieval

Retrieve molecular properties for compounds using either high-level or low-level approaches:

Using PubChemPy (Recommended):

import pubchempy as pcp

Get compound object with all properties

compound = pcp.get_compounds('caffeine', 'name')[0]

Access individual properties

molecular_formula = compound.molecular_formula molecular_weight = compound.molecular_weight iupac_name = compound.iupac_name smiles = compound.canonical_smiles inchi = compound.inchi xlogp = compound.xlogp # Partition coefficient tpsa = compound.tpsa # Topological polar surface area

Get Specific Properties:

Request only specific properties

properties = pcp.get_properties( ['MolecularFormula', 'MolecularWeight', 'CanonicalSMILES', 'XLogP'], 'aspirin', 'name' )

Returns list of dictionaries

Batch Property Retrieval:

import pandas as pd

compound_names = ['aspirin', 'ibuprofen', 'paracetamol'] all_properties = []

for name in compound_names: props = pcp.get_properties( ['MolecularFormula', 'MolecularWeight', 'XLogP'], name, 'name' ) all_properties.extend(props)

df = pd.DataFrame(all_properties)

Available Properties: MolecularFormula, MolecularWeight, CanonicalSMILES, IsomericSMILES, InChI, InChIKey, IUPACName, XLogP, TPSA, HBondDonorCount, HBondAcceptorCount, RotatableBondCount, Complexity, Charge, and many more (see references/api_reference.md for complete list).

3. Similarity Search

Find structurally similar compounds using Tanimoto similarity:

import pubchempy as pcp

Start with a query compound

query_compound = pcp.get_compounds('gefitinib', 'name')[0] query_smiles = query_compound.canonical_smiles

Perform similarity search

similar_compounds = pcp.get_compounds( query_smiles, 'smiles', searchtype='similarity', Threshold=85, # Similarity threshold (0-100) MaxRecords=50 )

Process results

for compound in similar_compounds[:10]: print(f"CID {compound.cid}: {compound.iupac_name}") print(f" MW: {compound.molecular_weight}")

Note: Similarity searches are asynchronous for large queries and may take 15-30 seconds to complete. PubChemPy handles the asynchronous pattern automatically.

4. Substructure Search

Find compounds containing a specific structural motif:

import pubchempy as pcp

Search for compounds containing pyridine ring

pyridine_smiles = 'c1ccncc1'

matches = pcp.get_compounds( pyridine_smiles, 'smiles', searchtype='substructure', MaxRecords=100 )

print(f"Found {len(matches)} compounds containing pyridine")

Common Substructures:

• Benzene ring: c1ccccc1

• Pyridine: c1ccncc1

• Phenol: c1ccc(O)cc1

• Carboxylic acid: C(=O)O

5. Format Conversion

Convert between different chemical structure formats:

import pubchempy as pcp

compound = pcp.get_compounds('aspirin', 'name')[0]

Convert to different formats

smiles = compound.canonical_smiles inchi = compound.inchi inchikey = compound.inchikey cid = compound.cid

Download structure files

pcp.download('SDF', 'aspirin', 'name', 'aspirin.sdf', overwrite=True) pcp.download('JSON', '2244', 'cid', 'aspirin.json', overwrite=True)

6. Structure Visualization

Generate 2D structure images:

import pubchempy as pcp

Download compound structure as PNG

pcp.download('PNG', 'caffeine', 'name', 'caffeine.png', overwrite=True)

Using direct URL (via requests)

import requests

cid = 2244 # Aspirin url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/PNG?image_size=large" response = requests.get(url)

with open('structure.png', 'wb') as f: f.write(response.content)

7. Synonym Retrieval

Get all known names and synonyms for a compound:

import pubchempy as pcp

synonyms_data = pcp.get_synonyms('aspirin', 'name')

if synonyms_data: cid = synonyms_data[0]['CID'] synonyms = synonyms_data[0]['Synonym']

print(f"CID {cid} has {len(synonyms)} synonyms:")
for syn in synonyms[:10]:  # First 10
    print(f"  - {syn}")

8. Bioactivity Data Access

Retrieve biological activity data from assays:

import requests import json

Get bioassay summary for a compound

cid = 2244 # Aspirin url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/assaysummary/JSON"

response = requests.get(url) if response.status_code == 200: data = response.json() # Process bioassay information table = data.get('Table', {}) rows = table.get('Row', []) print(f"Found {len(rows)} bioassay records")

For more complex bioactivity queries, use the scripts/bioactivity_query.py helper script which provides:

• Bioassay summaries with activity outcome filtering

• Assay target identification

• Search for compounds by biological target

• Active compound lists for specific assays

9. Comprehensive Compound Annotations

Access detailed compound information through PUG-View:

import requests

cid = 2244 url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON"

response = requests.get(url) if response.status_code == 200: annotations = response.json() # Contains extensive data including: # - Chemical and Physical Properties # - Drug and Medication Information # - Pharmacology and Biochemistry # - Safety and Hazards # - Toxicity # - Literature references # - Patents

Get Specific Section:

Get only drug information

url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/data/compound/{cid}/JSON?heading=Drug and Medication Information"

Installation Requirements

Install PubChemPy for Python-based access:

uv pip install pubchempy

For direct API access and bioactivity queries:

uv pip install requests

Optional for data analysis:

uv pip install pandas

Helper Scripts

This skill includes Python scripts for common PubChem tasks:

scripts/compound_search.py

Provides utility functions for searching and retrieving compound information:

Key Functions:

• search_by_name(name, max_results=10) : Search compounds by name

• search_by_smiles(smiles) : Search by SMILES string

• get_compound_by_cid(cid) : Retrieve compound by CID

• get_compound_properties(identifier, namespace, properties) : Get specific properties

• similarity_search(smiles, threshold, max_records) : Perform similarity search

• substructure_search(smiles, max_records) : Perform substructure search

• get_synonyms(identifier, namespace) : Get all synonyms

• batch_search(identifiers, namespace, properties) : Batch search multiple compounds

• download_structure(identifier, namespace, format, filename) : Download structures

• print_compound_info(compound) : Print formatted compound information

Usage:

from scripts.compound_search import search_by_name, get_compound_properties

Search for a compound

compounds = search_by_name('ibuprofen')

Get specific properties

props = get_compound_properties('aspirin', 'name', ['MolecularWeight', 'XLogP'])

scripts/bioactivity_query.py

Provides functions for retrieving biological activity data:

Key Functions:

• get_bioassay_summary(cid) : Get bioassay summary for compound

• get_compound_bioactivities(cid, activity_outcome) : Get filtered bioactivities

• get_assay_description(aid) : Get detailed assay information

• get_assay_targets(aid) : Get biological targets for assay

• search_assays_by_target(target_name, max_results) : Find assays by target

• get_active_compounds_in_assay(aid, max_results) : Get active compounds

• get_compound_annotations(cid, section) : Get PUG-View annotations

• summarize_bioactivities(cid) : Generate bioactivity summary statistics

• find_compounds_by_bioactivity(target, threshold, max_compounds) : Find compounds by target

Usage:

from scripts.bioactivity_query import get_bioassay_summary, summarize_bioactivities

Get bioactivity summary

summary = summarize_bioactivities(2244) # Aspirin print(f"Total assays: {summary['total_assays']}") print(f"Active: {summary['active']}, Inactive: {summary['inactive']}")

API Rate Limits and Best Practices

Rate Limits:

• Maximum 5 requests per second

• Maximum 400 requests per minute

• Maximum 300 seconds running time per minute

Best Practices:

• Use CIDs for repeated queries: CIDs are more efficient than names or structures

• Cache results locally: Store frequently accessed data

• Batch requests: Combine multiple queries when possible

• Implement delays: Add 0.2-0.3 second delays between requests

• Handle errors gracefully: Check for HTTP errors and missing data

• Use PubChemPy: Higher-level abstraction handles many edge cases

• Leverage asynchronous pattern: For large similarity/substructure searches

• Specify MaxRecords: Limit results to avoid timeouts

Error Handling:

from pubchempy import BadRequestError, NotFoundError, TimeoutError

try: compound = pcp.get_compounds('query', 'name')[0] except NotFoundError: print("Compound not found") except BadRequestError: print("Invalid request format") except TimeoutError: print("Request timed out - try reducing scope") except IndexError: print("No results returned")

Common Workflows

Workflow 1: Chemical Identifier Conversion Pipeline

Convert between different chemical identifiers:

import pubchempy as pcp

Start with any identifier type

compound = pcp.get_compounds('caffeine', 'name')[0]

Extract all identifier formats

identifiers = { 'CID': compound.cid, 'Name': compound.iupac_name, 'SMILES': compound.canonical_smiles, 'InChI': compound.inchi, 'InChIKey': compound.inchikey, 'Formula': compound.molecular_formula }

Workflow 2: Drug-Like Property Screening

Screen compounds using Lipinski's Rule of Five:

import pubchempy as pcp

def check_drug_likeness(compound_name): compound = pcp.get_compounds(compound_name, 'name')[0]

# Lipinski's Rule of Five
rules = {
    'MW <= 500': compound.molecular_weight <= 500,
    'LogP <= 5': compound.xlogp <= 5 if compound.xlogp else None,
    'HBD <= 5': compound.h_bond_donor_count <= 5,
    'HBA <= 10': compound.h_bond_acceptor_count <= 10
}

violations = sum(1 for v in rules.values() if v is False)
return rules, violations

rules, violations = check_drug_likeness('aspirin') print(f"Lipinski violations: {violations}")

Workflow 3: Finding Similar Drug Candidates

Identify structurally similar compounds to a known drug:

import pubchempy as pcp

Start with known drug

reference_drug = pcp.get_compounds('imatinib', 'name')[0] reference_smiles = reference_drug.canonical_smiles

Find similar compounds

similar = pcp.get_compounds( reference_smiles, 'smiles', searchtype='similarity', Threshold=85, MaxRecords=20 )

Filter by drug-like properties

candidates = [] for comp in similar: if comp.molecular_weight and 200 <= comp.molecular_weight <= 600: if comp.xlogp and -1 <= comp.xlogp <= 5: candidates.append(comp)

print(f"Found {len(candidates)} drug-like candidates")

Workflow 4: Batch Compound Property Comparison

Compare properties across multiple compounds:

import pubchempy as pcp import pandas as pd

compound_list = ['aspirin', 'ibuprofen', 'naproxen', 'celecoxib']

properties_list = [] for name in compound_list: try: compound = pcp.get_compounds(name, 'name')[0] properties_list.append({ 'Name': name, 'CID': compound.cid, 'Formula': compound.molecular_formula, 'MW': compound.molecular_weight, 'LogP': compound.xlogp, 'TPSA': compound.tpsa, 'HBD': compound.h_bond_donor_count, 'HBA': compound.h_bond_acceptor_count }) except Exception as e: print(f"Error processing {name}: {e}")

df = pd.DataFrame(properties_list) print(df.to_string(index=False))

Workflow 5: Substructure-Based Virtual Screening

Screen for compounds containing specific pharmacophores:

import pubchempy as pcp

Define pharmacophore (e.g., sulfonamide group)

pharmacophore_smiles = 'S(=O)(=O)N'

Search for compounds containing this substructure

hits = pcp.get_compounds( pharmacophore_smiles, 'smiles', searchtype='substructure', MaxRecords=100 )

Further filter by properties

filtered_hits = [ comp for comp in hits if comp.molecular_weight and comp.molecular_weight < 500 ]

print(f"Found {len(filtered_hits)} compounds with desired substructure")

Reference Documentation

For detailed API documentation, including complete property lists, URL patterns, advanced query options, and more examples, consult references/api_reference.md . This comprehensive reference includes:

• Complete PUG-REST API endpoint documentation

• Full list of available molecular properties

• Asynchronous request handling patterns

• PubChemPy API reference

• PUG-View API for annotations

• Common workflows and use cases

• Links to official PubChem documentation

Troubleshooting

Compound Not Found:

• Try alternative names or synonyms

• Use CID if known

• Check spelling and chemical name format

Timeout Errors:

• Reduce MaxRecords parameter

• Add delays between requests

• Use CIDs instead of names for faster queries

Empty Property Values:

• Not all properties are available for all compounds

• Check if property exists before accessing: if compound.xlogp:

• Some properties only available for certain compound types

Rate Limit Exceeded:

• Implement delays (0.2-0.3 seconds) between requests

• Use batch operations where possible

• Consider caching results locally

Similarity/Substructure Search Hangs:

• These are asynchronous operations that may take 15-30 seconds

• PubChemPy handles polling automatically

• Reduce MaxRecords if timing out

Additional Resources

• PubChem Home: https://pubchem.ncbi.nlm.nih.gov/

• PUG-REST Documentation: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest

• PUG-REST Tutorial: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial

• PubChemPy Documentation: https://pubchempy.readthedocs.io/

• PubChemPy GitHub: https://github.com/mcs07/PubChemPy