pubchem-database

Files (4)

• SKILL.md
• api_reference.md · references/
• bioactivity_query.py · scripts/
• compound_search.py · scripts/

SKILL.md (first lines)

# 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**:
```python
import pubchempy as pcp
compounds = pcp.get_compounds('aspirin', 'name')
compound = compounds[0]
```

**By CID (Compound ID)**:
```python
compound = pcp.Compound.from_cid(2244)  # Aspirin
```

**By SMILES**:
```python
compound = pcp.get_compounds('CC(=O)OC1=CC=CC=C1C(=O)O', 'smiles')[0]
```

**By InChI**:
```python
compound = pcp.get_compounds('InChI=1S/C9H8O4/...', 'inchi')[0]
```

**By Molecular Formula**:
```python
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)**:
```python
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**:
```python
# Request only specific properties
properties = pcp.get_properties(
    ['MolecularFormula', 'MolecularWeight', 'CanonicalSMILES', 'XLogP'],
    'aspirin',
    'name'
)
# Returns list of dictionaries
```

**Batch Property Retrieval**:
```python
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:

```python
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',

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