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