Pubchem Database

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:

pip install pubchempy

For direct API access and bioactivity queries:

pip install requests

Optional for data analysis:

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:

1. Use CIDs for repeated queries: CIDs are more efficient than names or structures
2. Cache results locally: Store frequently accessed data
3. Batch requests: Combine multiple queries when possible
4. Implement delays: Add 0.2-0.3 second delays between requests
5. Handle errors gracefully: Check for HTTP errors and missing data
6. Use PubChemPy: Higher-level abstraction handles many edge cases
7. Leverage asynchronous pattern: For large similarity/substructure searches
8. Specify MaxRecords: Limit results to avoid timeouts

Error Handling:

from pubchempy import BadRequestError, NotFoundError, TimeoutError