PubChemQuery

PubChemQuery: A Python Package for Accessing Chemical Information from PubChem.

PubChemQuery is a Python package that provides a simple and intuitive API for retrieving chemical information from the PubChem database. With this package, you can easily fetch chemical data, including:

• CID (Compound ID) by name

• All CIDs by name

• 2D images by CID or name

• SDF (Structure Data File) by CID or name

• Compound properties, including:

  • Molecular formula and weight

  • SMILES and InChI representations

  • IUPAC name and title

  • Physicochemical properties (e.g., XLogP, exact mass, TPSA)

  • Structural features (e.g., bond and atom counts, stereochemistry)

  • 3D properties (e.g., volume, steric quadrupole moments, feature counts)

  • Fingerprint and conformer information

The package offers a straightforward interface, allowing users to access PubChem data with minimal code. Whether you're a chemist, researcher, or developer, PubChemQuery simplifies the process of integrating chemical information into your projects.

Key Features:

Retrieve chemical data by name or CID

Access 2D images and SDF files

Get compound properties, including physicochemical, structural, and 3D features

Easy-to-use API with minimal code required

Simple and Concise API:

There are functions that perform all of the above-mentioned tasks, making it easy to integrate PubChem data into your projects:

• get_cid_by_inchi(inchi): Get a CID by InChI

• get_cids_by_formula(formula): Get CIDs by formula

• get_cid_by_name(name): Get CID by name

• get_cids_by_name(name): Get all CIDs by name

• get_image_by_cid(cid): Get 2D image by CID

• get_image_by_name(name): Get 2D image by name

• get_image_by_inchi(inchi): Get 2D image by InChI

• get_structure_by_cid(cid): Get SDF by CID

• get_structure_by_name(name): Get SDF by name

• get_similar_structures_cids_by_compound_id(cid/SMILES/InChI): Get similar structures CIDs by cid, SMILES, InChI

Compound Object:

The package also includes a Compound object that encapsulates the retrieved data, providing a convenient way

to access and manipulate the data.

• compound(cid_or_name): Create a compound object with properties and methods

Getting Started:

To use PubChemQuery, simply install the package and import it into your Python script. Refer to the example code snippets above for a quick start.

Installation

Install PubChemQuery with pip

  pip install PubChemQuery

Examples

Import package as:

import pubchemquery as pcq

Use the functions to retrieve data:

# get a cid by formula

cid = pcq.get_cids_by_formula('C6H6')

print(type(cid), len(cid))

# get a cid by inchi

cid = pcq.get_cid_by_inchi(

    'InChI=1S/C6H5NO3/c8-6-3-1-5(2-4-6)7(9)10/h1-4,8H')

print(cid)

# get a cid by name

cid = pcq.get_cid_by_name('benzene')

print(cid)

# get all cids by name

cids = pcq.get_cids_by_name('benzene')

print(type(cids), len(cids))

# get 2d image

# by cid

image = pcq.get_image_by_cid('241')

image



# by name

image = pcq.get_image_by_name('benzene')

image



# by inchi

image = pcq.get_image_by_inchi(

    'InChI=1S/C6H5NO3/c8-6-3-1-5(2-4-6)7(9)10/h1-4,8H')

print(image)

# get sdf by cid

sdf = pcq.get_structure_by_cid('241')

print(sdf)

# get sdf by name

sdf = pcq.get_structure_by_name('benzene')

print(sdf)

# get similar structure cids by cid

# cids = pcq.get_similar_structures_cids_by_compound_id('241')

# cids = pcq.get_similar_structures_cids_by_compound_id(

#     'C1=CC=CC=C1', compound_id='SMILES')

cids = pcq.get_similar_structures_cids_by_compound_id(

    'InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H', compound_id='InChI')

print(type(cids), len(cids))

Make a compound and then get its properties:

# make a compound

cid = 2244

# compound = pcq.compound(cid)

# name

name = '2-acetyloxybenzoic acid'

compound = pcq.compound(name)

print(compound)

# properties

# InChI

print(compound.InChI)

# InChIKey

print(compound.InChIKey)

# IUPACName

print(compound.IUPACName)

# similar structure cids

print(len(compound.similar_structure_cids))

# image

compound.image

# dataframe

compound.prop_df()

FAQ

For any question, contact me on LinkedIn

Authors

• @sinagilassi