pax_global_header00006660000000000000000000000064150576365660014535gustar00rootroot0000000000000052 comment=9935a14e7fdb4a88d27a99fedce69ca99f004698 mcs07-PubChemPy-1fcc2e5/000077500000000000000000000000001505763656600150125ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/.github/000077500000000000000000000000001505763656600163525ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/.github/workflows/000077500000000000000000000000001505763656600204075ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/.github/workflows/code-check.yml000066400000000000000000000015301505763656600231160ustar00rootroot00000000000000name: Code Check on: push: branches: [main] pull_request: branches: [main] jobs: code-check: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.11" - name: Install uv uses: astral-sh/setup-uv@v6 with: version: "0.8.4" enable-cache: true - name: Install dependencies run: uv sync --locked --group lint - name: Check imports with ruff run: uv run ruff check --select I --exit-non-zero-on-fix . - name: Check formatting with ruff run: uv run ruff format --check . - name: Clean notebooks run: uv run nb-clean check --remove-empty-cells --remove-all-notebook-metadata --preserve-cell-outputs examples/*.ipynb mcs07-PubChemPy-1fcc2e5/.github/workflows/release.yml000066400000000000000000000043231505763656600225540ustar00rootroot00000000000000name: Release on: push: tags: - "v*" workflow_dispatch: concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: false permissions: contents: read jobs: build: name: Build dists runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.11" - name: Install uv uses: astral-sh/setup-uv@v6 with: version: "0.8.4" enable-cache: true - name: Verify tag matches project version run: | VER=$(uv version --short) TAG=${GITHUB_REF_NAME#v} test "$VER" = "$TAG" || { echo "Tag $TAG != version $VER"; exit 1; } - name: Build sdist and wheel with uv run: uv build --no-sources - name: Upload dist artifacts uses: actions/upload-artifact@v4 with: name: dist path: dist/* publish: name: Publish to PyPI needs: build runs-on: ubuntu-latest environment: name: pypi url: https://pypi.org/p/PubChemPy permissions: id-token: write contents: read steps: - name: Download dist artifacts uses: actions/download-artifact@v4 with: name: dist path: dist - name: Publish package distributions to PyPI uses: pypa/gh-action-pypi-publish@release/v1 release: name: Create GitHub Release needs: publish runs-on: ubuntu-latest permissions: contents: write steps: - uses: actions/checkout@v4 - name: Download dist artifacts uses: actions/download-artifact@v4 with: name: dist path: dist - name: Create GitHub Release (draft) env: GH_TOKEN: ${{ github.token }} TAG: ${{ github.ref_name }} run: | args=( "${TAG}" dist/* --title "PubChemPy ${TAG#v}" --generate-notes --draft ) if gh release view "${TAG}" >/dev/null 2>&1; then echo "Release ${TAG} already exists; skipping create." else gh release create "${args[@]}" fi mcs07-PubChemPy-1fcc2e5/.github/workflows/test.yml000066400000000000000000000017711505763656600221170ustar00rootroot00000000000000name: Tests on: push: branches: [ main ] pull_request: branches: [ main ] # Cancel previous runs on the same branch concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true jobs: test: name: test (py${{ matrix.python-version }}, ${{ matrix.os }}) runs-on: ${{ matrix.os }} strategy: fail-fast: false # max-parallel: 1 matrix: python-version: ["3.10", "3.11", "3.12", "3.13"] os: [ubuntu-latest, macos-latest, windows-latest] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install uv uses: astral-sh/setup-uv@v6 with: version: "0.8.4" enable-cache: true - name: Install dependencies run: uv sync --locked --all-extras --group test - name: Run tests run: uv run pytest tests -v mcs07-PubChemPy-1fcc2e5/.gitignore000066400000000000000000000015661505763656600170120ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Sphinx documentation docs/_build/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # mypy .mypy_cache/ .dmypy.json dmypy.json # pytype static type analyzer .pytype/ # PyCharm .idea/ # VS Code .vscode/* !.vscode/settings.json !.vscode/tasks.json !.vscode/launch.json !.vscode/extensions.json !.vscode/*.code-snippets mcs07-PubChemPy-1fcc2e5/.pre-commit-config.yaml000066400000000000000000000016621505763656600213000ustar00rootroot00000000000000repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v5.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-toml - id: check-json - id: check-added-large-files - id: check-merge-conflict - id: check-shebang-scripts-are-executable - id: debug-statements - repo: https://github.com/abravalheri/validate-pyproject rev: v0.24.1 hooks: - id: validate-pyproject - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.12.5 hooks: - id: ruff-check args: - --fix - --select - I - --exit-non-zero-on-fix - id: ruff-format - repo: https://github.com/srstevenson/nb-clean rev: 4.0.1 hooks: - id: nb-clean args: - --remove-empty-cells - --remove-all-notebook-metadata - --preserve-cell-outputs mcs07-PubChemPy-1fcc2e5/.readthedocs.yaml000066400000000000000000000003251505763656600202410ustar00rootroot00000000000000version: 2 build: os: ubuntu-22.04 tools: python: "3.12" sphinx: configuration: docs/conf.py formats: all python: install: - requirements: docs/requirements.txt - method: pip path: . mcs07-PubChemPy-1fcc2e5/.vscode/000077500000000000000000000000001505763656600163535ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/.vscode/extensions.json000066400000000000000000000001341505763656600214430ustar00rootroot00000000000000{ "recommendations": [ "charliermarsh.ruff", "ms-python.python" ] } mcs07-PubChemPy-1fcc2e5/.vscode/settings.json000066400000000000000000000011671505763656600211130ustar00rootroot00000000000000{ "python.testing.pytestArgs": [ "tests", "-s", "-vv" ], "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, "files.insertFinalNewline": true, "files.trimTrailingWhitespace": true, "files.trimFinalNewlines": true, "python.defaultInterpreterPath": "./.venv/bin/python", "[python]": { "editor.defaultFormatter": "charliermarsh.ruff", "editor.rulers": [88] }, "[shellscript]": { "editor.tabSize": 2 }, "[markdown]": { "editor.wordWrap": "wordWrapColumn", "editor.wordWrapColumn": 88 } } mcs07-PubChemPy-1fcc2e5/CITATION.cff000066400000000000000000000021321505763656600167020ustar00rootroot00000000000000cff-version: 1.2.0 message: "If you use this software in your work, please cite it using the following metadata." title: "PubChemPy" type: software authors: - family-names: "Swain" given-names: "Matthew" orcid: "https://orcid.org/0000-0001-6428-5189" version: 1.0.4 doi: "10.5281/zenodo.541438" date-released: "2017-04-11" repository-code: "https://github.com/mcs07/PubChemPy" url: "https://pubchempy.org" repository-artifact: "https://pypi.org/project/PubChemPy/" license: "MIT" license-url: "https://spdx.org/licenses/MIT.html" identifiers: - type: doi description: Fixed concept DOI for all versions value: "10.5281/zenodo.593126" abstract: Python package for interacting with the PubChem database via the PUG REST API service. keywords: - cheminformatics - chemistry - API - python - pubchem preferred-citation: type: software title: "PubChemPy" authors: - family-names: "Swain" given-names: "Matthew" orcid: "https://orcid.org/0000-0001-6428-5189" version: 1.0.4 date-released: "2017-04-11" doi: "10.5281/zenodo.541438" url: "https://pubchempy.org" mcs07-PubChemPy-1fcc2e5/LICENSE000066400000000000000000000020601505763656600160150ustar00rootroot00000000000000The MIT License Copyright 2014-2025 Matt Swain Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. mcs07-PubChemPy-1fcc2e5/README.md000066400000000000000000000047651505763656600163050ustar00rootroot00000000000000# PubChemPy [![PyPI Version](https://img.shields.io/pypi/v/PubChemPy?logo=python&logoColor=%23ffffff)](https://pypi.python.org/pypi/PubChemPy) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/pubchempy?logo=anaconda&logoColor=%23ffffff)](https://anaconda.org/conda-forge/pubchempy) [![License](https://img.shields.io/pypi/l/PubChemPy)](https://github.com/mcs07/PubChemPy/blob/main/LICENSE) [![DOI](https://zenodo.org/badge/7462957.svg)](https://zenodo.org/badge/latestdoi/7462957) [![Tests](https://img.shields.io/github/actions/workflow/status/mcs07/pubchempy/test.yml?logo=github&logoColor=%23ffffff&label=tests)](https://github.com/mcs07/PubChemPy/actions/workflows/test.yml) [![Docs](https://img.shields.io/readthedocs/pubchempy?logo=readthedocs&logoColor=%23ffffff)](https://docs.pubchempy.org) PubChemPy provides a way to interact with PubChem in Python. It allows chemical searches by name, substructure and similarity, chemical standardization, conversion between chemical file formats, depiction and retrieval of chemical properties. ## Installation Install PubChemPy with pip: ```shell pip install pubchempy ``` Or with conda: ```shell conda install -c conda-forge pubchempy ``` For detailed instructions, see the [installation guide](https://docs.pubchempy.org/en/latest/guide/install.html). ## Example usage Retrieve a compound by its PubChem Compound Identifier (CID) and print its SMILES and IUPAC name: ```pycon >>> import pubchempy as pcp >>> comp = pcp.Compound.from_cid(1423) >>> print(comp.smiles) CCCCCCCNC1CCCC1CCCCCCC(=O)O >>> print(comp.iupac_name) 7-[2-(heptylamino)cyclopentyl]heptanoic acid ``` Search compounds by name and print the SMILES and molecular weight of the first result: ```pycon >>> results = pcp.get_compounds("Aspirin", "name") >>> print(results[0].smiles) CC(=O)OC1=CC=CC=C1C(=O)O >>> print(results[0].molecular_weight) 180.16 ``` ## Documentation Full documentation is available at . This includes a [step-by-step guide on how to use PubChemPy](https://docs.pubchempy.org/en/latest/guide/gettingstarted.html), as well as a [complete API reference](https://docs.pubchempy.org/en/latest/api.html). ## Contributing - Feature ideas and bug reports are welcome on the [Issue Tracker](https://github.com/mcs07/PubChemPy/issues). - Fork the [source code](https://github.com/mcs07/PubChemPy) on GitHub, make changes and file a pull request. ## License PubChemPy is licensed under the [MIT license](https://github.com/mcs07/PubChemPy/blob/main/LICENSE). mcs07-PubChemPy-1fcc2e5/codemeta.json000066400000000000000000000025111505763656600174650ustar00rootroot00000000000000{ "@context": "https://w3id.org/codemeta/3.0", "type": "SoftwareSourceCode", "applicationCategory": "http://edamontology.org/topic_2258", "author": [ { "id": "https://orcid.org/0000-0001-6428-5189", "type": "Person", "email": "m.swain@me.com", "familyName": "Swain", "givenName": "Matthew" } ], "codeRepository": "https://github.com/mcs07/PubChemPy", "dateCreated": "2013-01-05", "dateModified": "2017-04-11", "datePublished": "2014-01-06", "description": "Python package for interacting with the PubChem database via the PUG REST API service.", "identifier": "https://doi.org/10.5281/zenodo.541438", "keywords": [ "cheminformatics", "chemistry", "API", "python", "pubchem" ], "license": "https://spdx.org/licenses/MIT", "name": "PubChemPy", "programmingLanguage": "Python", "softwareVersion": "1.0.5", "continuousIntegration": "https://github.com/mcs07/PubChemPy/actions", "readme": "https://github.com/mcs07/PubChemPy/blob/main/README.md", "url": "https://docs.pubchempy.org", "issueTracker": "https://github.com/mcs07/PubChemPy/issues", "relatedLink": [ "https://pypi.org/project/PubChemPy/", "https://docs.pubchempy.org" ] } mcs07-PubChemPy-1fcc2e5/docs/000077500000000000000000000000001505763656600157425ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/docs/Makefile000066400000000000000000000011721505763656600174030ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) mcs07-PubChemPy-1fcc2e5/docs/api.md000066400000000000000000000071371505763656600170450ustar00rootroot00000000000000--- tocdepth: '3' --- (api)= # API documentation This part of the documentation is automatically generated from the PubChemPy source code and comments. It contains comprehensive information on every function, class and method available in the PubChemPy library. ```{eval-rst} .. module:: pubchempy ``` ## Search functions ```{eval-rst} .. autofunction:: get_compounds .. autofunction:: get_substances .. autofunction:: get_assays .. autofunction:: get_properties .. autofunction:: get_synonyms ``` ## Objects The PubChem database is organized into three main record types: - **Substances**: Raw chemical records deposited by data contributors. - **Compounds**: Standardized and deduplicated chemical records derived from substances. - **Assays**: Experimental data from biological screening and testing. PubChemPy has classes to represent each of these record types. ```{eval-rst} .. autoclass:: pubchempy.Compound :members: .. autoclass:: pubchempy.Atom :members: .. autoclass:: pubchempy.Bond :members: .. autoclass:: pubchempy.Substance :members: .. autoclass:: pubchempy.Assay :members: ``` ## Identifier functions ```{eval-rst} .. autofunction:: get_cids .. autofunction:: get_sids .. autofunction:: get_aids ``` ## Request functions ```{eval-rst} .. autofunction:: download .. autofunction:: request .. autofunction:: get .. autofunction:: get_json .. autofunction:: get_sdf ``` ## *pandas* functions Each of the search functions, {func}`~pubchempy.get_compounds`, {func}`~pubchempy.get_substances` and {func}`~pubchempy.get_properties` has an `as_dataframe` parameter. When set to `True`, these functions automatically extract properties from each result in the list into a pandas {class}`~pandas.DataFrame` and return that instead of the results themselves. If you already have a list of Compounds or Substances, the functions below allow a {class}`~pandas.DataFrame` to be constructed easily. ```{eval-rst} .. autofunction:: compounds_to_frame .. autofunction:: substances_to_frame ``` ## Constants ```{eval-rst} .. autodata:: pubchempy.API_BASE .. autodata:: pubchempy.ELEMENTS .. autodata:: pubchempy.PROPERTY_MAP .. autoclass:: pubchempy.CompoundIdType :members: .. autoclass:: pubchempy.BondType :members: .. autoclass:: pubchempy.CoordinateType :members: .. autoclass:: pubchempy.ProjectCategory :members: ``` ## Exceptions ```{eval-rst} .. autoexception:: pubchempy.PubChemPyError :show-inheritance: .. autoexception:: pubchempy.ResponseParseError :show-inheritance: .. autoexception:: pubchempy.PubChemHTTPError :show-inheritance: .. autoexception:: pubchempy.BadRequestError :show-inheritance: .. autoexception:: pubchempy.NotFoundError :show-inheritance: .. autoexception:: pubchempy.MethodNotAllowedError :show-inheritance: .. autoexception:: pubchempy.ServerError :show-inheritance: .. autoexception:: pubchempy.UnimplementedError :show-inheritance: .. autoexception:: pubchempy.ServerBusyError :show-inheritance: .. autoexception:: pubchempy.TimeoutError :show-inheritance: .. autoexception:: pubchempy.PubChemPyDeprecationWarning :show-inheritance: ``` ## Changes - As of v1.0.3, the `atoms` and `bonds` properties on {class}`Compounds ` now return lists of {class}`~pubchempy.Atom` and {class}`~pubchempy.Bond` objects, rather than dicts. - As of v1.0.2, search functions now return an empty list instead of raising a {class}`~pubchempy.NotFoundError` exception when no results are found. {class}`~pubchempy.NotFoundError` is still raised when attempting to create a {class}`~pubchempy.Compound` using the `from_cid` class method with an invalid CID. mcs07-PubChemPy-1fcc2e5/docs/conf.py000066400000000000000000000036101505763656600172410ustar00rootroot00000000000000"""Configuration file for the Sphinx documentation builder.""" # # For the full list of built-in configuration values, see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html import os import sys # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. sys.path.insert(0, os.path.abspath("../src")) # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = "PubChemPy" copyright = "2014-2025, Matt Swain" author = "Matt Swain" release = "1.0.5" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration extensions = [ "sphinx.ext.autodoc", "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx.ext.ifconfig", "myst_parser", ] templates_path = ["_templates"] exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "furo" html_static_path = ["_static"] html_theme_options = { "source_repository": "https://github.com/mcs07/PubChemPy", "source_branch": "main", "source_directory": "docs/", } # -- Options for autodoc ---------------------------------------------------- # Sort autodoc members by the order they appear in the source code autodoc_member_order = "bysource" # Concatenate the class and __init__ docstrings together autoclass_content = "both" # -- Options for intersphinx ------------------------------------------------- intersphinx_mapping = { "python": ("https://docs.python.org/3/", None), "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None), } mcs07-PubChemPy-1fcc2e5/docs/guide/000077500000000000000000000000001505763656600170375ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/docs/guide/advanced.md000066400000000000000000000120771505763656600211350ustar00rootroot00000000000000(advanced)= # Advanced usage This guide covers advanced PubChemPy usage patterns, API best practices, error handling, logging, and low-level request functions. (avoiding_timeouterror)= ## Avoiding TimeoutError If there are too many results for a request, you will receive a TimeoutError. There are different ways to avoid this, depending on what type of request you are doing. If retrieving full compound or substance records, instead request a list of cids or sids for your input, and then request the full records for those identifiers individually or in small groups. For example: ```python sids = get_sids("Aspirin", "name") for sid in sids: s = Substance.from_sid(sid) ``` When using the `formula` namespace or a `searchtype`, you can also alternatively use the `listkey_count` and `listkey_start` keyword arguments to specify pagination. The `listkey_count` value specifies the number of results per page, and the `listkey_start` value specifies which page to return. For example: ```python get_compounds("CC", "smiles", searchtype="substructure", listkey_count=5) get("C10H21N", "formula", listkey_count=3, listkey_start=6) ``` ## Logging PubChemPy can generate logging statements if required. The logger is named 'pubchempy'. Set the desired logging level globally for all loggers: ```python import logging logging.basicConfig(level=logging.DEBUG) ``` Or specifically enable the PubChempy logger: ```python console_handler = logging.StreamHandler() console_handler.setFormatter(logging.Formatter(logging.BASIC_FORMAT)) pcp_logger = logging.getLogger("pubchempy") pcp_logger.setLevel(logging.DEBUG) pcp_logger.addHandler(console_handler) ``` The levels, in order of increasing verbosity, are CRITICAL, ERROR, WARNING, INFO, DEBUG. There is more information on logging in the [Python logging documentation][python logging documentation]. ## Using behind a proxy When using PubChemPy behind a proxy, you may receive a `URLError`: ``` URLError: ``` A simple fix is to specify the proxy information via urllib: ```python import urllib proxy_support = urllib.request.ProxyHandler({ "http": "http://:", "https": "https://:" }) opener = urllib.request.build_opener(proxy_support) urllib.request.install_opener(opener) ``` ## Custom requests If you wish to perform more complicated requests, you can use the {func}`~pubchempy.request` function. This is an extremely simple wrapper around the REST API that allows you to construct any sort of request from a few parameters. The [PUG REST Specification] has all the information you will need to formulate your requests. The {func}`~pubchempy.request` function simply returns the exact response from the PubChem server as a string. This can be parsed in different ways depending on the output format you choose. See the Python [json], [xml] and [csv] packages for more information. Additionally, cheminformatics toolkits such as [Open Babel] and [RDKit] offer tools for handling SDF files in Python. The {func}`~pubchempy.get` function is very similar to the {func}`~pubchempy.request` function, except it handles `listkey` type responses automatically for you. This makes things simpler, however it means you can't take advantage of using the same `listkey` repeatedly to obtain different types of information. See the [PUG REST specification] for more information on how `listkey` responses work. ### Summary of possible inputs ``` = list of cid, sid, aid, source, inchikey, listkey; string of name, smiles, xref, inchi, sdf; = substance | compound | assay compound domain = cid | name | smiles | inchi | sdf | inchikey | | | listkey | formula = record | property/[comma-separated list of property tags] | synonyms | sids | cids | aids | assaysummary | classification substance domain = sid | sourceid/ | sourceall/ | name | | listkey = record | synonyms | sids | cids | aids | assaysummary | classification assay domain = aid | listkey | type/ | sourceall/ = all | confirmatory | doseresponse | onhold | panel | rnai | screening | summary = record | aids | sids | cids | description | targets/{ProteinGI, ProteinName, GeneID, GeneSymbol} | doseresponse/sid = {substructure | superstructure | similarity | identity}/{smiles | inchi | sdf | cid} = xref/{RegistryID | RN | PubMedID | MMDBID | ProteinGI | NucleotideGI | TaxonomyID | MIMID | GeneID | ProbeID | PatentID} = XML | ASNT | ASNB | JSON | JSONP [ ?callback= ] | SDF | CSV | PNG | TXT ``` [csv]: https://docs.python.org/3/library/csv.html [json]: https://docs.python.org/3/library/json.html [open babel]: https://openbabel.org/docs/UseTheLibrary/Python.html [pug rest specification]: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest [python logging documentation]: https://docs.python.org/3/howto/logging.html [rdkit]: https://www.rdkit.org [xml]: https://docs.python.org/3/library/xml.etree.elementtree.html mcs07-PubChemPy-1fcc2e5/docs/guide/compound.md000066400000000000000000000035521505763656600212120ustar00rootroot00000000000000(compound)= # Compounds The {func}`~pubchempy.get_compounds` function returns a list of {class}`~pubchempy.Compound` objects. You can also instantiate a {class}`~pubchempy.Compound` object directly if you know its CID: ```python c = pcp.Compound.from_cid(6819) ``` ## Dictionary representation Each {class}`~pubchempy.Compound` has a `record` property, which is a dictionary that contains the all the information about the compound, produced exactly from the JSON response from the PubChem API. All other properties are derived from this record. Additionally, each {class}`~pubchempy.Compound` provides a {meth}`~pubchempy.Compound.to_dict` method that returns PubChemPy's own dictionary representation of the Compound data. As well as being more concisely formatted than the raw `record`, this method also takes an optional parameter to filter the list of the desired properties: ```pycon >>> c = pcp.Compound.from_cid(962) >>> c.to_dict(properties=["atoms", "bonds", "inchi"]) {'atoms': [{'aid': 1, 'element': 'o', 'x': 2.5369, 'y': -0.155}, {'aid': 2, 'element': 'h', 'x': 3.0739, 'y': 0.155}, {'aid': 3, 'element': 'h', 'x': 2, 'y': 0.155}], 'bonds': [{'aid1': 1, 'aid2': 2, 'order': 'single'}, {'aid1': 1, 'aid2': 3, 'order': 'single'}], 'inchi': u'InChI=1S/H2O/h1H2'} ``` ## 3D compounds By default, compounds are returned with 2D coordinates. Use the `record_type` keyword argument to specify otherwise: ```python pcp.get_compounds("Aspirin", "name", record_type="3d") ``` Many properties are missing from 3D records, and the following properties are *only* available on 3D records: - `volume_3d` - `multipoles_3d` - `conformer_rmsd_3d` - `effective_rotor_count_3d` - `pharmacophore_features_3d` - `mmff94_partial_charges_3d` - `mmff94_energy_3d` - `conformer_id_3d` - `shape_selfoverlap_3d` - `feature_selfoverlap_3d` - `shape_fingerprint_3d` mcs07-PubChemPy-1fcc2e5/docs/guide/contribute.md000066400000000000000000000005461505763656600215440ustar00rootroot00000000000000(contribute)= # Contributing The [Issue Tracker] is the best place to post any feature ideas, requests and bug reports. If you are able to contribute changes yourself, just fork the [source code] on GitHub, make changes and open a pull request. [issue tracker]: https://github.com/mcs07/PubChemPy/issues [source code]: https://github.com/mcs07/PubChemPy mcs07-PubChemPy-1fcc2e5/docs/guide/download.md000066400000000000000000000012101505763656600211620ustar00rootroot00000000000000(download)= # Download The {func}`~pubchempy.download` function is for saving a file to disk. The following formats are available: XML, ASNT/B, JSON, SDF, CSV, PNG, TXT. Beware that not all formats are available for all types of information. SDF and PNG are only available for full Compound and Substance records, and CSV is best suited to tables of properties and identifiers. Examples: ```python pcp.download("PNG", "asp.png", "Aspirin", "name") pcp.download("CSV", "s.csv", [1,2,3], operation="property/ConnectivitySMILES,SMILES") ``` For PNG images, the `image_size` argument can be used to specify `large`, `small` or `x`. mcs07-PubChemPy-1fcc2e5/docs/guide/gettingstarted.md000066400000000000000000000056361505763656600224230ustar00rootroot00000000000000(gettingstarted)= # Getting started This page gives a introduction on how to get started with PubChemPy. This assumes you already have PubChemPy {ref}`installed `. ## Retrieving a Compound Retrieving information about a specific Compound in the PubChem database is simple. Begin by importing PubChemPy: ```pycon >>> import pubchempy as pcp ``` Let's get the {class}`~pubchempy.Compound` with [CID 5090]: ```pycon >>> c = pcp.Compound.from_cid(5090) ``` Now we have a {class}`~pubchempy.Compound` object called `c`. We can get all the information we need from this object: ```pycon >>> print(c.molecular_formula) C17H14O4S >>> print(c.molecular_weight) 314.4 >>> print(c.smiles) CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3 >>> print(c.xlogp) 2.3 >>> print(c.iupac_name) 3-(4-methylsulfonylphenyl)-4-phenyl-2H-furan-5-one >>> print(c.synonyms) ['rofecoxib', 'Vioxx', 'Ceoxx', '162011-90-7', 'MK 966', ... ] ``` ````{note} All the code examples in this documentation will assume you have imported PubChemPy as `pcp`. If you prefer, you can alternatively import specific functions and classes by name and use them directly: ```python from pubchempy import Compound, get_compounds c = Compound.from_cid(1423) cs = get_compounds("Aspirin", "name") ``` ```` ## Searching What if you don't know the PubChem CID of the Compound you want? Just use the {func}`~pubchempy.get_compounds` function, for example with a compound name input: ```pycon >>> results = pcp.get_compounds("Glucose", "name") >>> print(results) [Compound(5793)] ``` The first argument is the identifier, and the second argument is the identifier type, which must be one of `name`, `smiles`, `sdf`, `inchi`, `inchikey` or `formula`. More often than not, only a single result will be returned, but sometimes there are multiple results for a given identifier. Therefore, {func}`~pubchempy.get_compounds` returns a list of {class}`~pubchempy.Compound` objects (even if there is only one result). It is possible to iterate over this list to get the individual {class}`~pubchempy.Compound` objects: ```pycon >>> for compound in results: ... print(compound.smiles) C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O ``` Or you can access the first result directly: ```pycon >>> compound = results[0] >>> print(compound.smiles) C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O ``` Retrieving the compound record(s) for a SMILES input is just as easy: ```pycon >>> pcp.get_compounds("C1=CC2=C(C3=C(C=CC=N3)C=C2)N=C1", "smiles") [Compound(1318)] ``` ```{note} Beware that line notation inputs like SMILES and InChI can return automatically generated records that aren't actually present in PubChem, and therefore have no CID and are missing many properties that are too complicated to calculate on the fly. ``` That's all the most basic things you can do with PubChemPy. Read on for more some more advanced usage examples. [cid 5090]: https://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=5090 mcs07-PubChemPy-1fcc2e5/docs/guide/install.md000066400000000000000000000044251505763656600210340ustar00rootroot00000000000000(install)= # Installation PubChemPy supports Python versions 3.10+. There are no required dependencies. There are a variety of ways to download and install PubChemPy. ## Option 1: Use pip (recommended) The easiest and recommended way to install is using pip, the package installer for Python. It comes included with most modern Python distrubtions and you can use it to install packages from the [Python Package Index]. Install PubChemPy using pip by running the following command in your terminal or command prompt: ```shell pip install pubchempy ``` This will download the latest version of PubChemPy, and place it in your `site-packages` folder so it is automatically available to all your python scripts. If pip is missing from your Python distribution, you can [install it by following the official guide][install it by following the official guide]. ## Option 2: Use conda Conda is a cross-platform package manager that is popular in the scientific Python community. It provides an alternative to pip for installing Python packages and managing environments. Conda can be installed using the [Miniforge] installer, which is free and open-source, or the [Anaconda] installer, which is a commercial distribution that includes many scientific packages by default. Once you have conda installed, you can install PubChmePy from the conda-forge channel with the following command: ```shell conda install -c conda-forge pubchempy ``` The conda-forge channel is a community-driven collection of conda packages that provides up-to-date and well-maintained packages for the conda package manager. ## Option 3: Clone the repository The latest development version of PubChemPy is always [available on GitHub]. This version is not guaranteed to be stable, but may include new features that have not yet been released. Simply clone the repository and install as usual: ```shell git clone https://github.com/mcs07/PubChemPy.git cd PubChemPy pip install . ``` [anaconda]: https://www.anaconda.com/download [available on github]: https://github.com/mcs07/PubChemPy [download the latest release]: https://github.com/mcs07/PubChemPy/releases/ [install it by following the official guide]: https://pip.pypa.io/en/stable/installation/ [miniforge]: https://conda-forge.org/download/ [python package index]: https://pypi.org/ mcs07-PubChemPy-1fcc2e5/docs/guide/introduction.md000066400000000000000000000032421505763656600221030ustar00rootroot00000000000000(introduction)= # Introduction ## How PubChemPy works PubChemPy relies entirely on the PubChem database and chemical toolkits provided via their PUG REST web service [^f1]. This service provides an interface for programs to automatically carry out the tasks that you might otherwise perform manually via the [PubChem website]. This is important to remember when using PubChemPy: Every request you make is transmitted to the PubChem servers, evaluated, and then a response is sent back. There are some downsides to this: It is less suitable for confidential work, it requires a constant internet connection, and some tasks will be slower than if they were performed locally on your own computer. On the other hand, this means we have the vast resources of the PubChem database and chemical toolkits at our disposal. As a result, it is possible to do complex similarity and substructure searching against a database containing tens of millions of compounds in seconds, without needing any of the storage space or computational power on your own local computer. See the {doc}`pugrest` page for more information about how PubChemPy uses the PubChem web service. ## PubChemPy license ```{eval-rst} .. include:: ../../LICENSE ``` ```{rubric} Footnotes ``` [^f1]: That's a lot of acronyms! PUG stands for "Power User Gateway", a term used to describe a variety of methods for programmatic access to PubChem data and services. REST stands for [Representational State Transfer], which describes the specific architectural style of the web service. [pubchem website]: https://pubchem.ncbi.nlm.nih.gov [representational state transfer]: https://en.wikipedia.org/wiki/Representational_state_transfer mcs07-PubChemPy-1fcc2e5/docs/guide/pandas.md000066400000000000000000000020741505763656600206320ustar00rootroot00000000000000(pandas)= # *pandas* integration ## Installing *pandas* *pandas* must be installed to use its functionality from within PubChemPy. It is an optional dependency, so it is not installed automatically with PubChemPy. The easiest way is to use pip: ```bash pip install pandas ``` See the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/) for more information. ## Usage It is possible for {func}`~pubchempy.get_compounds`, {func}`~pubchempy.get_substances` and {func}`~pubchempy.get_properties` to return a pandas DataFrame: ```python df1 = pcp.get_compounds("C20H41Br", "formula", as_dataframe=True) df2 = pcp.get_substances([1, 2, 3, 4], as_dataframe=True) df3 = pcp.get_properties(["smiles", "xlogp", "rotatable_bond_count"], "C20H41Br", "formula", as_dataframe=True) ``` An existing list of {class}`~pubchempy.Compound` objects can be converted into a dataframe, optionally specifying the desired columns: ```python cs = pcp.get_compounds("C20H41Br", "formula") df4 = pcp.compounds_to_frame(cs, properties=["smiles", "xlogp", "rotatable_bond_count"]) ``` mcs07-PubChemPy-1fcc2e5/docs/guide/properties.md000066400000000000000000000046621505763656600215650ustar00rootroot00000000000000(properties)= # Properties The {func}`~pubchempy.get_properties` function allows the retrieval of specific properties without having to deal with entire compound records. This is especially useful for retrieving the properties of a large number of compounds at once: ```python p = pcp.get_properties("SMILES", "CC", "smiles", searchtype="superstructure") ``` Multiple properties may be specified in a list, or in a comma-separated string. The available properties are: MolecularFormula, MolecularWeight, ConnectivitySMILES, SMILES, InChI, InChIKey, IUPACName, XLogP, ExactMass, MonoisotopicMass, TPSA, Complexity, Charge, HBondDonorCount, HBondAcceptorCount, RotatableBondCount, HeavyAtomCount, IsotopeAtomCount, AtomStereoCount, DefinedAtomStereoCount, UndefinedAtomStereoCount, BondStereoCount, DefinedBondStereoCount, UndefinedBondStereoCount, CovalentUnitCount, Volume3D, XStericQuadrupole3D, YStericQuadrupole3D, ZStericQuadrupole3D, FeatureCount3D, FeatureAcceptorCount3D, FeatureDonorCount3D, FeatureAnionCount3D, FeatureCationCount3D, FeatureRingCount3D, FeatureHydrophobeCount3D, ConformerModelRMSD3D, EffectiveRotorCount3D, ConformerCount3D. ## Synonyms Get a list of synonyms for a given input using the {func}`~pubchempy.get_synonyms` function: ```python pcp.get_synonyms("Aspirin", "name") pcp.get_synonyms("Aspirin", "name", "substance") ``` Inputs that match more than one SID/CID will have multiple, separate synonyms lists returned. ## CAS Registry Numbers CAS Registry Numbers are not officially supported by PubChem, but they are often present in the synonyms associated with a compound. Therefore it is straightforward to retrieve them by filtering the synonyms to just those with the CAS Registry Number format: ```python for result in pcp.get_synonyms("Aspirin", "name"): cid = result["CID"] cas_rns = [] for syn in result.get("Synonym", []): match = re.match(r"(\d{2,7}-\d\d-\d)", syn) if match: cas_rns.append(match.group(1)) print(f"CAS registry numbers for CID {cid}: {cas_rns}") ``` ## Identifiers There are three functions for getting a list of identifiers for a given input: - {func}`~pubchempy.get_cids` - {func}`~pubchempy.get_sids` - {func}`~pubchempy.get_aids` For example, passing a CID to {func}`~pubchempy.get_sids` will return a list of SIDs corresponding to the {class}`~pubchempy.Substance` records that were standardised and merged to produce the given {class}`~pubchempy.Compound`. mcs07-PubChemPy-1fcc2e5/docs/guide/pugrest.md000066400000000000000000000053451505763656600210610ustar00rootroot00000000000000(pugrest)= # PUG REST PUG (Power User Gateway) REST is a web service that PubChem provides for programmatic access to its data. PubChemPy uses this web service to interact with the PubChem database, allowing you to search for compounds, substances, and assays, retrieve their properties, and perform various operations without needing to download or store large datasets locally. You don't need to worry too much about how the PubChem web service works, because PubChemPy handles all of the details for you. But understanding the underlying architecture can help you use PubChemPy more effectively and troubleshoot issues. ## PUG REST architecture The PUG REST API is built around a three-part request pattern: 1. **Input**: Specifies which records you're interested in (by CID, name, SMILES, etc.) 2. **Operation**: Defines what to do with those records (retrieve properties, search, etc.) 3. **Output**: Determines the format of the returned data (JSON, XML, CSV, etc.) This modular design allows for flexible combinations. For example, you can combine structure input via SMILES with property retrieval operations and CSV output - all handled seamlessly by PubChemPy. ## Request flow When you make a request with PubChemPy: 1. Your Python request is translated into a PUG REST URL (and possibly some POST data). 2. The request is sent to PubChem's servers via HTTPS. 3. PubChem processes the request using their chemical databases and toolkits. 4. Results are returned and parsed by PubChemPy into Python objects. PubChem contains over 300 million substance records, over 100 million standardized compound records, and over 1 million biological assays. All this data may be accessed and processed through PubChemPy without requiring local storage or computational resources. ## When to use alternatives While PubChemPy and PUG REST are excellent for many tasks, consider alternatives for: - **Bulk data processing**: Use PubChem's bulk download services for large datasets - **Confidential work**: Consider local chemical toolkits for sensitive data - **Offline work**: The PUG REST API requires an internet connection ## Further reading If you want to go beyond the capabilities of PubChemPy, there is helpful documentation about programmatic access to PubChem data on the PubChem website: - [Programmatic Access to PubChem](https://pubchem.ncbi.nlm.nih.gov/docs/programmatic-access): Overview of how to access PubChem data programmatically. - [PUG REST Tutorial](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest): Explains how the web service works with a variety of usage examples. - [PUG REST Specification](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial): A more comprehensive but dense specification that details every possible way to use the web service. mcs07-PubChemPy-1fcc2e5/docs/guide/searching.md000066400000000000000000000051541505763656600213310ustar00rootroot00000000000000(searching)= # Searching PubChemPy provides powerful search capabilities that leverage PubChem's extensive chemical databases. Understanding the different search types and their performance characteristics can help you choose the most efficient approach for your needs. By default, requests look for an exact match with the input. Alternatively, you can specify a search type using the `searchtype` parameter to perform chemical substructure, superstructure, similarity, or identity searches. ```python pcp.get_compounds("CC", "smiles", searchtype="superstructure", listkey_count=3) ``` The `listkey_count` and `listkey_start` arguments can be used for pagination. Each `searchtype` has its own options that can be specified as keyword arguments. For example, similarity searches have a `Threshold`, and super/substructure searches have `MatchIsotopes`. A full list of options is available in the [PUG REST Specification]. Note: These types of search are *slow*. ## Getting a full results list for common compound names For some very common names, PubChem maintains a filtered whitelist of human-chosen CIDs with the intention of reducing confusion about which is the 'right' result. In the past, a search for Glucose would return four different results, each with different stereochemistry information. But now, a single result is returned, which has been chosen as 'correct' by the PubChem team. Unfortunately it isn't directly possible to return to the previous behaviour, but there is a straightforward workaround: Search for Substances with that name (which are completely unfiltered) and then get the compounds that are derived from those substances. There area a few different ways you can do this using PubChemPy, but the easiest is probably using the {func}`~pubchempy.get_cids` function: > ```pycon > >>> pcp.get_cids("2-nonenal", "name", "substance", list_return="flat") > [17166, 5283335, 5354833] > ``` This searches the substance database for '2-nonenal', and gets the CID for the compound associated with each substance. By default, this returns a mapping between each SID and CID, but the `list_return='flat'` parameter flattens this into just a single list of unique CIDs. You can then use {meth}`~pubchempy.Compound.from_cid` to get the full {class}`~pubchempy.Compound` record, equivalent to what is returned by {func}`~pubchempy.get_compounds`: > ```pycon > >>> cids = pcp.get_cids("2-nonenal", "name", "substance", list_return="flat") > >>> [pcp.Compound.from_cid(cid) for cid in cids] > [Compound(17166), Compound(5283335), Compound(5354833)] > ``` [pug rest specification]: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST.html mcs07-PubChemPy-1fcc2e5/docs/guide/substance.md000066400000000000000000000033471505763656600213570ustar00rootroot00000000000000(substance)= # Substances The PubChem Substance database contains all chemical records deposited in PubChem in their most raw form, before any significant processing is applied. As a result, it contains duplicates, mixtures, and some records that don't make chemical sense. This means that {class}`~pubchempy.Substance` records contain fewer calculated properties, however they do have additional information about the original source that deposited the record. The PubChem Compound database is constructed from the Substance database using a standardization and deduplication process. Hence each {class}`~pubchempy.Compound` may be derived from a number of different {class}`~pubchempy.Substance` records. ## Retrieving substances Retrieve Substances using the {func}`~pubchempy.get_substances` function: ```pycon >>> results = pcp.get_substances("Coumarin 343", "name") >>> print(results) [Substance(24864499), Substance(85084977), Substance(126686397), Substance(143491255), Substance(152243230), Substance(162092514), Substance(162189467), Substance(186021999), Substance(206257050), ... ] ``` You can also instantiate a {class}`~pubchempy.Substance` directly from its SID: ```pycon >>> substance = pcp.Substance.from_sid(223766453) >>> print(substance.synonyms) ['2-(Acetyloxy)-benzoic acid', '2-(acetyloxy)benzoic acid', '2-acetoxy benzoic acid', '2-acetoxy-benzoic acid', '2-acetoxybenzoic acid', '2-acetyloxybenzoic acid', 'BSYNRYMUTXBXSQ-UHFFFAOYSA-N', 'acetoxybenzoic acid', 'acetyl salicylic acid', 'acetyl-salicylic acid', 'acetylsalicylic acid', 'aspirin', 'o-acetoxybenzoic acid'] >>> print(substance.source_id) BSYNRYMUTXBXSQ-UHFFFAOYSA-N >>> print(substance.standardized_cid) 2244 >>> print(substance.standardized_compound) Compound(2244) ``` mcs07-PubChemPy-1fcc2e5/docs/index.md000066400000000000000000000057261505763656600174050ustar00rootroot00000000000000# PubChemPy documentation PubChemPy provides a way to interact with the PubChem database in Python, via the PUG REST API. PubChem is a comprehensive chemical information resource that contains millions of chemical structures and their associated biological, physical, and toxicological data. This package handles the complexity of the PubChem PUG REST API, providing a simple Pythonic interface for chemical informatics workflows. It allows retrieval of chemical structures, properties, and experimental data, including molecular descriptors, fingerprints, 3D conformer data, and biological assay results. It also enables chemical searches by name, substructure and similarity, conversion between different chemical formats, chemical standardization, and depiction. Here's a quick example showing how to get calculated properties for a specific compound: ```pycon >>> import pubchempy as pcp >>> compound = pcp.Compound.from_cid(2244) # Aspirin >>> print(compound.molecular_formula) C9H8O4 >>> print(compound.iupac_name) 2-acetyloxybenzoic acid >>> print(compound.molecular_weight) 180.16 >>> print(compound.xlogp) 1.2 ``` Here's how to search for a compound by name: ```pycon >>> for compound in pcp.get_compounds("glucose", "name"): ... print(compound.cid) ... print(compound.smiles) ... 5793 C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O ``` All the heavy lifting is done by PubChem's servers, using their database and chemical toolkits. ## Features - Search PubChem Substance and Compound databases by name, SMILES, InChI and SDF. - Retrieve the standardised Compound record for a given input structure. - Convert between SDF, SMILES, InChI, PubChem CID and more. - Retrieve calculated properties, fingerprints and descriptors. - Generate 2D and 3D coordinates. - Get IUPAC systematic names, trade names and all known synonyms for a given Compound. - Download compound records as XML, ASNT/B, JSON, SDF and depiction as a PNG image. - Construct property tables using *pandas* DataFrames. - A complete Python wrapper around the [PubChem PUG REST web service]. - Supports Python versions 3.9+. ## Useful links - Source code is available on [GitHub]. - Ask a question or report a bug on the [Issue Tracker]. - PUG REST API [tutorial] and [documentation]. ## User guide A step-by-step guide to getting started with PubChemPy. ```{toctree} :maxdepth: 2 guide/introduction guide/install guide/gettingstarted guide/searching guide/compound guide/substance guide/properties guide/pandas guide/download guide/advanced guide/pugrest guide/contribute ``` ## API documentation Comprehensive API documentation with information on every function, class and method. ```{toctree} :maxdepth: 2 api ``` [documentation]: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest [GitHub]: https://github.com/mcs07/PubChemPy [Issue Tracker]: https://github.com/mcs07/PubChemPy/issues [PubChem PUG REST web service]: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest [tutorial]: https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial mcs07-PubChemPy-1fcc2e5/docs/requirements.txt000066400000000000000000000000611505763656600212230ustar00rootroot00000000000000furo>=2025.7.19 myst-parser>=3.0.1 sphinx>=7.4.7 mcs07-PubChemPy-1fcc2e5/examples/000077500000000000000000000000001505763656600166305ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/examples/1-introduction.ipynb000066400000000000000000000070051505763656600225540ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PubChemPy examples\n", "\n", "## Table of Contents\n", "\n", "- [1. Introduction](1-introduction.ipynb)\n", "- [2. Getting Started](2-getting-started.ipynb)\n", "\n", "# 1. Introduction\n", "\n", "PubChemPy provides a way to interact with PubChem in Python. It allows chemical searches by name, substructure and similarity, chemical standardization, conversion between chemical file formats, depiction and retrieval of chemical properties.\n", "\n", "Here’s a quick example showing how to search for a compound by name:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5793\n", "C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O\n" ] } ], "source": [ "from pubchempy import get_compounds\n", "\n", "for compound in get_compounds(\"glucose\", \"name\"):\n", " print(compound.cid)\n", " print(compound.smiles)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So how does this work behind the scenes?\n", "\n", "1. We call the PubChemPy function `get_compounds` with the parameters `'glucose'` and `'name'`\n", "2. This is translated into [a request](https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/glucose/JSON) for the PubChem PUG REST API.\n", "3. PubChemPy parses the JSON response into a list of `Compound` objects.\n", "4. Each `Compound` has properties like `cid` and `smiles`, which we print.\n", "\n", "Here’s how you get calculated properties for a specific compound:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C17H14O4S\n", "314.4\n", "2.3\n" ] } ], "source": [ "from pubchempy import Compound\n", "\n", "vioxx = Compound.from_cid(5090)\n", "print(vioxx.molecular_formula)\n", "print(vioxx.molecular_weight)\n", "print(vioxx.xlogp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using PubChemPy, it is important to remember that every request you make is transmitted to the PubChem servers, evaluated, and then a response is sent back. There are some downsides to this: It is less suitable for confidential work, it requires a constant internet connection, and some tasks will be slower than if they were performed locally on your own computer. On the other hand, this means we have the vast resources of the PubChem database and chemical toolkits at our disposal. As a result, it is possible to do complex similarity and substructure searching against a database containing tens of millions of compounds in seconds, without needing any of the storage space or computational power on your own local computer.\n", "\n", "You don’t need to worry too much about how the PubChem web service works, because PubChemPy handles all of the details for you. But if you want to go beyond the capabilities of PubChemPy, there is some [helpful documentation on the PubChem website](https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST_Tutorial.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Next: [Getting Started](2-getting-started.ipynb)" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 1 } mcs07-PubChemPy-1fcc2e5/examples/2-getting-started.ipynb000066400000000000000000000214431505763656600231430ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PubChemPy examples\n", "\n", "## Table of Contents\n", "\n", "- [1. Introduction](1-introduction.ipynb)\n", "- [2. Getting Started](2-getting-started.ipynb)\n", "\n", "# 2. Getting Started\n", "\n", "## Retrieving a Compound\n", "\n", "Retrieving information about a specific Compound in the PubChem database is simple.\n", "\n", "Begin by importing PubChemPy:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pubchempy as pcp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s get the Compound with [CID 5090](https://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=5090):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Compound(5090)" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = pcp.Compound.from_cid(5090)\n", "c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a `Compound` object called `c`. We can get all the information we need from this object:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C17H14O4S\n" ] } ], "source": [ "print(c.molecular_formula)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "314.4\n" ] } ], "source": [ "print(c.molecular_weight)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3\n" ] } ], "source": [ "print(c.smiles)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.3\n" ] } ], "source": [ "print(c.xlogp)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3-(4-methylsulfonylphenyl)-4-phenyl-2H-furan-5-one\n" ] } ], "source": [ "print(c.iupac_name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['rofecoxib', '162011-90-7', 'Vioxx', 'Ceoxx', 'MK 966', '4-(4-(Methylsulfonyl)phenyl)-3-phenylfuran-2(5H)-one', 'Vioxx Dolor', 'MK-966', '4-[4-(methylsulfonyl)phenyl]-3-phenyl-2(5H)-furanone', '4-[4-(methylsulfonyl)phenyl]-3-phenylfuran-2(5H)-one', 'MK0966', 'MK-0966', 'rofecoxibum', 'TRM-201', '0QTW8Z7MCR', 'NSC-720256', 'NSC-758705', 'CHEBI:8887', 'TRM201', 'DTXSID2023567', 'M01AH02', '3-phenyl-4-[4-(methylsulfonyl)phenyl]-2(5H)-furanone', '4-(4-(Methylsulfonyl)phenyl)-3-phenyl-2(5H)-furanone', '4-(4-methylsulfonylphenyl)-3-phenyl-5H-furan-2-one', 'DTXCID903567', 'NSC720256', '3-phenyl-4-(4-(methylsulfonyl)phenyl)-2(5H)-furanone', \"4-(4'-(Methylsulfonyl)phenyl)-3-phenyl-2(5H)-furanone\", '803-260-0', 'MK 0966', '3-(4-methylsulfonylphenyl)-4-phenyl-2H-furan-5-one', '2(5H)-Furanone, 4-[4-(methylsulfonyl)phenyl]-3-phenyl-', 'MK966', '4-(4-methanesulfonylphenyl)-3-phenyl-2,5-dihydrofuran-2-one', 'MFCD00935806', 'CHEMBL122', '4-(p-(Methylsulfonyl)phenyl)-3-phenyl-2(5H)-furanone', 'refecoxib', 'NCGC00095118-01', 'Vioxx-d5 (Major)', '2(5H)-Furanone, 4-(4-(methylsulfonyl)phenyl)-3-phenyl-', 'C17H14O4S', 'Vioxx (trademark)', 'SMR000466331', 'CCRIS 8967', 'Vioxx (TN)', 'HSDB 7262', 'SR-01000762904', 'UNII-0QTW8Z7MCR', 'Rofecoxib (JAN/USAN/INN)', 'Rofecoxib [USAN:INN:BAN]', '3-Phenyl-4-(4-(methylsulfonyl)phenyl))-2(5H)-furanone', 'Rofecoxib (Vioxx)', 'Rofecoxib [USAN]', 'Rofecoxib (Standard)', 'KS-1107', 'MK 0996', 'Spectrum_000119', 'ROFECOXIB [INN]', 'ROFECOXIB [JAN]', 'SpecPlus_000669', 'ROFECOXIB [MI]', 'ROFECOXIB [HSDB]', 'Spectrum2_000446', 'Spectrum3_001153', 'Spectrum4_000631', 'Spectrum5_001598', 'ROFECOXIB [VANDF]', 'ROFECOXIB [MART.]', 'ROFECOXIB [WHO-DD]', 'SCHEMBL3050', 'BSPBio_002705', 'KBioGR_001242', 'KBioGR_002345', 'KBioSS_000559', 'KBioSS_002348', 'MLS000759440', 'MLS001165770', 'MLS001195623', 'MLS001424113', 'MLS006010091', 'BIDD:GT0399', 'DivK1c_006765', 'SPECTRUM1504235', 'Rofecoxib - Bio-X trade mark', 'SPBio_000492', '3-(4-methanesulfonylphenyl)-2-phenyl-2-buten-4-olide', 'GTPL2893', 'orb1309482', 'ROFECOXIB [ORANGE BOOK]', 'BDBM22369', 'KBio1_001709', 'KBio2_000559', 'KBio2_002345', 'KBio2_003127', 'KBio2_004913', 'KBio2_005695', 'KBio2_007481', 'KBio3_002205', 'KBio3_002825', 'EX-A708', 'cMAP_000024', 'GLXC-05865', 'HMS1922H11', 'HMS2051G16', 'HMS2089H20', 'HMS2093E04', 'HMS2232G21', 'HMS3371P11', 'HMS3393G16', 'HMS3651F16', 'HMS3713B07', 'HMS3750I17', 'HMS3885E05', 'HMS5081K08', 'Pharmakon1600-01504235', 'BCP03619', 'MSK10084', 'UWA68493', 'Tox21_111430', 'CCG-40253', 'HY-17372R', 'NSC758705', 's3043', 'STK635144', 'AKOS000280931', 'AB07701', 'CS-0997', 'DB00533', 'FV28711', 'NC00132', 'NSC 720256', 'NSC 758705', 'SB19518', 'NCGC00095118-02', 'NCGC00095118-03', 'NCGC00095118-04', 'NCGC00095118-05', 'NCGC00095118-08', 'NCGC00095118-17', 'NCGC00095118-18', 'AC-28318', 'BR164362', 'HY-17372', 'NCI60_041175', 'SBI-0206774.P001', 'CAS-162011-90-7', 'NS00003940', 'R0206', 'SW219668-1', 'C07590', 'D00568', 'AB00052090-06', 'AB00052090-08', 'AB00052090_09', 'AB00052090_10', 'EN300-7364304', 'L000912', 'Q411412', 'SR-01000762904-3', 'SR-01000762904-5', 'BRD-K21733600-001-02-6', 'BRD-K21733600-001-06-7', 'BRD-K21733600-001-14-1', 'BRD-K21733600-001-15-8', 'BRD-K21733600-001-19-0', 'BRD-K21733600-001-23-2', 'BRD-K21733600-001-24-0', '3-(4-methanesulfonyl-phenyl)-2-phenyl-2-buten-4-olide', 'Z2037279770', '2(5H)-Furanone, 4-[4-(methyl-sulfonyl)phenyl]-3-phenyl-', '3-(Phenyl)-4-(4-(methylsulfonyl)phenyl)-2-(5H)-furanone', '3-Phenyl-4-(4-(Methylsulfonyl)Phenyl)-2-(5H)-Furanone', '4-(4-METHANESULFONYL-PHENYL)-3-PHENYL-5H-FURAN-2-ONE', '4-(4-methylsulfonylphenyl)-3-phenyl-2,5-dihydro-2-furanone', '4-4-(Methylsulfonyl)phenyl-3-phenyl-2(5H)-furanone;Rofecoxib']\n" ] } ], "source": [ "print(c.synonyms)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Searching\n", "\n", "What if you don’t know the PubChem CID of the Compound you want? Just use the `get_compounds()` function:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Compound(16051935)]" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = pcp.get_compounds(\"Morphine sulfate\", \"name\")\n", "results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first argument is the identifier, and the second argument is the identifier type, which must be one of name, smiles, sdf, inchi, inchikey or formula. It looks like there are 4 compounds in the PubChem Database that have the name Glucose associated with them. Let’s take a look at them in more detail:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C([C@@H]1[C@H]([C@@H]([C@H](C(O1)O)O)O)O)O\n" ] } ], "source": [ "for compound in results:\n", " print(compound.smiles)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like they all have different stereochemistry information.\n", "\n", "Retrieving the record for a SMILES string is just as easy:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pcp.get_compounds(\"C1=CC2=C(C3=C(C=CC=N3)C=C2)N=C1\", \"smiles\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's worth being aware that line notation inputs like SMILES and InChI can return automatically generated records that aren’t actually present in PubChem, and therefore have no CID and are missing many properties that are too complicated to calculate on the fly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Previous: [Introduction](1-introduction.ipynb)" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 1 } mcs07-PubChemPy-1fcc2e5/examples/CAS registry numbers.ipynb000066400000000000000000000203421505763656600235670ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Retrieving CAS registry numbers\n", "\n", "CAS Registry numbers are not officially supported as compound metadata or a property type in PubChem. However, in many instances, CAS registry numbers are present in the collection of name synonyms associated with a compound. Therefore it is straightforward to retrieve them by getting compound synonyms and then filtering these to just those with the CAS registry number format." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import re\n", "import time\n", "\n", "import pubchempy as pcp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Optionally enable debug logging to make it easier to see what is going on:\n", "# logging.basicConfig(level=logging.DEBUG)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A function to get CAS registry numbers by filtering a list of compound synonyms:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def filter_cas_rns(synonyms: list[str]) -> list[str]:\n", " \"\"\"Filter a list of synonyms to just those that look like CAS registry numbers.\"\"\"\n", " cas_rns = []\n", " for syn in synonyms:\n", " match = re.match(r\"(\\d{2,7}-\\d\\d-\\d)\", syn)\n", " if match:\n", " cas_rns.append(match.group(1))\n", " return cas_rns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CAS Registry Numbers for a given PubChem CID" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CAS registry numbers for CID 2242: ['25548-16-7']\n" ] } ], "source": [ "for result in pcp.get_synonyms(2242):\n", " cid = result[\"CID\"]\n", " cas_rns = filter_cas_rns(result.get(\"Synonym\", []))\n", " print(f\"CAS registry numbers for CID {cid}: {cas_rns}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CAS Registry Numbers for a batch of PubChem CIDs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CAS registry numbers for CID 2242: ['25548-16-7']\n", "CAS registry numbers for CID 134601: ['22839-47-0', '53906-69-7', '7421-84-3']\n", "CAS registry numbers for CID 6992065: ['22839-65-2']\n" ] } ], "source": [ "for result in pcp.get_synonyms([2242, 134601, 6992065]):\n", " cid = result[\"CID\"]\n", " cas_rns = filter_cas_rns(result.get(\"Synonym\", []))\n", " print(f\"CAS registry numbers for CID {cid}: {cas_rns}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CAS Registry Numbers for a PubChemPy Compound object" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CAS registry numbers for CID 2242: ['25548-16-7']\n" ] } ], "source": [ "compound = pcp.Compound.from_cid(2242)\n", "cas_rns = filter_cas_rns(compound.synonyms)\n", "print(f\"CAS registry numbers for CID 2242: {cas_rns}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CAS Registry Numbers for substructure search results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CAS registry numbers for CID 134601: ['22839-47-0', '53906-69-7', '7421-84-3']\n", "CAS registry numbers for CID 9810996: ['165450-17-9']\n", "CAS registry numbers for CID 2242: ['25548-16-7']\n", "CAS registry numbers for CID 6992066: []\n", "CAS registry numbers for CID 56843846: ['714229-20-6', '245650-17-3']\n", "CAS registry numbers for CID 3804937: []\n", "CAS registry numbers for CID 25130065: ['106372-55-8']\n", "CAS registry numbers for CID 6992065: ['22839-65-2']\n", "CAS registry numbers for CID 14060789: []\n", "CAS registry numbers for CID 44364601: []\n" ] } ], "source": [ "count = 0\n", "for result in pcp.get_synonyms(\n", " \"COC(=O)C(CC1=CC=CC=C1)NC(=O)C(CC(=O)O)N\", \"smiles\", searchtype=\"substructure\"\n", "):\n", " cid = result[\"CID\"]\n", " cas_rns = filter_cas_rns(result.get(\"Synonym\", []))\n", " print(f\"CAS registry numbers for CID {cid}: {cas_rns}\")\n", " count += 1\n", " if count >= 10:\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could potentially get a TimeoutError if there are too many results. In this case, it might be better to perform the substructure search and then get the synonyms individually for each result or in batches:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Getting synonyms for batch: [24290, 23938, 5702536, 62732, 167845]\n", "CAS registry numbers for CID 24290: ['7647-10-1']\n", "CAS registry numbers for CID 23938: ['7440-05-3', '7440-05-3', '7440-05-3']\n", "CAS registry numbers for CID 5702536: ['12107-56-1', '12193-11-2']\n", "CAS registry numbers for CID 62732: ['19168-23-1', '13820-55-8']\n", "CAS registry numbers for CID 167845: ['3375-31-3', '19807-27-3']\n", "Getting synonyms for batch: [11979704, 9811564, 74855, 24932, 61732]\n", "CAS registry numbers for CID 11979704: ['14221-01-3']\n", "CAS registry numbers for CID 9811564: ['51364-51-3', '60748-47-2']\n", "CAS registry numbers for CID 74855: ['2035-66-7']\n", "CAS registry numbers for CID 24932: ['10102-05-3']\n", "CAS registry numbers for CID 61732: ['14323-43-4', '13782-33-7']\n", "Getting synonyms for batch: [73974, 161205, 424947, 153931, 166846]\n", "CAS registry numbers for CID 73974: ['11113-77-2']\n", "CAS registry numbers for CID 161205: ['16970-55-1']\n", "CAS registry numbers for CID 424947: ['7790-38-7', '90-38-7']\n", "CAS registry numbers for CID 153931: ['134620-00-1']\n", "CAS registry numbers for CID 166846: ['13566-03-5', '22723-63-3']\n", "Getting synonyms for batch: [5486778, 53384484, 171041442, 517722, 3035388]\n", "CAS registry numbers for CID 5486778: ['14024-61-4']\n", "CAS registry numbers for CID 53384484: ['14024-61-4']\n", "CAS registry numbers for CID 171041442: ['759457-82-4', '886845-72-3']\n", "CAS registry numbers for CID 517722: ['14024-61-4']\n", "CAS registry numbers for CID 3035388: ['14024-61-4']\n", "Getting synonyms for batch: [6102075, 61538, 168754, 5462715, 50930385]\n", "CAS registry numbers for CID 6102075: ['13965-03-2']\n", "CAS registry numbers for CID 61538: ['12012-95-2']\n", "CAS registry numbers for CID 168754: ['14852-83-6', '14409-60-0', '14708-52-2', '28068-05-5', '14286-03-4']\n", "CAS registry numbers for CID 5462715: ['24-61-4', '14024-61-4']\n", "CAS registry numbers for CID 50930385: ['14024-61-4']\n" ] } ], "source": [ "cids = pcp.get_cids(\"[Pd]\", \"smiles\", searchtype=\"substructure\")\n", "batch_size = 5\n", "for i in range(0, len(cids), batch_size):\n", " batch = cids[i : i + batch_size]\n", " print(f\"Getting synonyms for batch: {batch}\")\n", " results = pcp.get_synonyms(batch)\n", " for result in results:\n", " cid = result[\"CID\"]\n", " cas_rns = filter_cas_rns(result.get(\"Synonym\", []))\n", " print(f\"CAS registry numbers for CID {cid}: {cas_rns}\")\n", " time.sleep(1) # Respect PubChem's rate limits\n", " if i >= 20:\n", " break" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } mcs07-PubChemPy-1fcc2e5/examples/Chemical fingerprints and similarity.ipynb000066400000000000000000000223461505763656600267740ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chemical similarity using PubChem fingerprints" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import Image\n", "\n", "import pubchempy as pcp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we'll get some compounds. Here we just use PubChem CIDs to retrieve, but you could search (e.g. using name, SMILES, SDF, etc.)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coumarin = pcp.Compound.from_cid(323)\n", "Image(url=\"https://pubchem.ncbi.nlm.nih.gov/image/imgsrv.fcgi?cid=323&t=l\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coumarin_314 = pcp.Compound.from_cid(72653)\n", "Image(url=\"https://pubchem.ncbi.nlm.nih.gov/image/imgsrv.fcgi?cid=72653&t=l\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coumarin_343 = pcp.Compound.from_cid(108770)\n", "Image(url=\"https://pubchem.ncbi.nlm.nih.gov/image/imgsrv.fcgi?cid=108770&t=l\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aspirin = pcp.Compound.from_cid(2244)\n", "Image(url=\"https://pubchem.ncbi.nlm.nih.gov/image/imgsrv.fcgi?cid=2244&t=l\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The similarity between two molecules is typically calculated using molecular fingerprints that encode structural information about the molecule as a series of bits (0 or 1). These bits represent the presence or absence of particular patterns or substructures — two molecules that contain more of the same patterns will have more bits in common, indicating that they are more similar.\n", "\n", "The PubChem CACTVS fingerprint is available on each compound using the `fingerprint` method. This is returned as a hex-encoded string:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0000037180703000000000000000000000000000000000000000304000000000000000810000001A00000000000C04809800300E80000400880220D208000208002020000888000608C80C262284311A823A20A4C01108A98780C0200E00000000000800000000000000100000000000000000'" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coumarin.fingerprint" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can decode this from hexadecimal and then display as a binary string as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0b1101110001100000000111000000110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011000001000000000000000000000000000000000000000000000000000000000000001000000100000000000000000000000000011010000000000000000000000000000000000000000000001100000001001000000010011000000000000011000000001110100000000000000000000100000000001000100000000010001000001101001000001000000000000000001000001000000000000010000000100000000000000000100010001000000000000000011000001000110010000000110000100110001000101000010000110001000110101000001000111010001000001010010011000000000100010000100010101001100001111000000011000000001000000000111000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000'" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bin(int(coumarin.fingerprint, 16))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is more information about the PubChem fingerprints at \n", "\n", "The most commonly used measure for quantifying the similarity of two fingerprints is the Tanimoto Coefficient, given by:\n", "\n", "$$ T = \\frac{N_{ab}}{N_{a} + N_{b} - N_{ab}} $$\n", "\n", "where $N_{a}$ and $N_{b}$ are the number of 1-bits (i.e corresponding to the presence of a pattern) in the fingerprints of molecule $a$ and molecule $b$ respectively. $N_{ab}$ is the number of 1-bits common to the fingerprints of both molecule $a$ and $b$. The Tanimoto coefficient ranges from 0 when the fingerprints have no bits in common, to 1 when the fingerprints are identical.\n", "\n", "Here's a simple way to calculate the Tanimoto coefficient between two compounds in python:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def tanimoto(compound1, compound2):\n", " fp1 = int(compound1.fingerprint, 16)\n", " fp2 = int(compound2.fingerprint, 16)\n", " fp1_count = bin(fp1).count(\"1\")\n", " fp2_count = bin(fp2).count(\"1\")\n", " both_count = bin(fp1 & fp2).count(\"1\")\n", " return float(both_count) / (fp1_count + fp2_count - both_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try it out:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tanimoto(coumarin, coumarin)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6011904761904762" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tanimoto(coumarin, coumarin_314)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6011904761904762" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tanimoto(coumarin, coumarin_343)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9529411764705882" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tanimoto(coumarin_314, coumarin_343)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8211382113821138" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tanimoto(coumarin, aspirin)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6123595505617978" ] }, "execution_count": null, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tanimoto(coumarin_343, aspirin)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a nice simple method, but not particularly efficient. If you are looking for better performance, check out Andrew Dalke's work:\n", "\n", "- [Computing Tanimoto scores, quickly](http://www.dalkescientific.com/writings/diary/archive/2008/06/27/computing_tanimoto_scores.html)\n", "- [chemfp](http://chemfp.com)" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 1 } mcs07-PubChemPy-1fcc2e5/pubchempy.py000066400000000000000000002256111505763656600173670ustar00rootroot00000000000000"""PubChemPy: Python Interface for the PubChem Database.""" from __future__ import annotations import enum import functools import json import logging import os import ssl import time import typing as t import warnings from http.client import HTTPResponse from itertools import zip_longest from urllib.error import HTTPError from urllib.parse import quote, urlencode from urllib.request import urlopen if t.TYPE_CHECKING: import pandas as pd # Get SSL certs from env var or certifi package if available. _CA_FILE = os.getenv("PUBCHEMPY_CA_BUNDLE") or os.getenv("REQUESTS_CA_BUNDLE") if not _CA_FILE: try: import certifi _CA_FILE = certifi.where() except ImportError: _CA_FILE = None __author__ = "Matt Swain" __email__ = "m.swain@me.com" __version__ = "1.0.5" __license__ = "MIT" __all__ = [ # Main API functions "get_compounds", "get_substances", "get_assays", "get_properties", "get_synonyms", "get_cids", "get_sids", "get_aids", "get_all_sources", "download", "request", "get", "get_json", "get_sdf", # Core classes "Compound", "Substance", "Assay", "Atom", "Bond", # Enum/constant classes "CompoundIdType", "BondType", "CoordinateType", "ProjectCategory", # Data conversion functions "compounds_to_frame", "substances_to_frame", # Constants "API_BASE", "ELEMENTS", "PROPERTY_MAP", # Exceptions "PubChemPyError", "ResponseParseError", "PubChemHTTPError", "BadRequestError", "NotFoundError", "MethodNotAllowedError", "ServerError", "UnimplementedError", "ServerBusyError", "TimeoutError", "PubChemPyDeprecationWarning", ] #: Base URL for the PubChem PUG REST API. API_BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" log = logging.getLogger("pubchempy") log.addHandler(logging.NullHandler()) #: Type alias for URL query parameters. QueryParam = str | int | float | bool | list[str] | None class CompoundIdType(enum.IntEnum): """Compound record type.""" #: Original Deposited Compound DEPOSITED = 0 #: Standardized Form of a Deposited Compound STANDARDIZED = 1 #: Component of a Standardized Compound COMPONENT = 2 #: Neutralized Form of a Standardized Compound NEUTRALIZED = 3 #: Substance that is a component of a mixture MIXTURE = 4 #: Predicted Tautomer Form TAUTOMER = 5 #: Predicted Ionized pKa Form IONIZED = 6 #: Unknown Compound Type UNKNOWN = 255 class BondType(enum.IntEnum): """Bond Type Information.""" #: Single Bond SINGLE = 1 #: Double Bond DOUBLE = 2 #: Triple Bond TRIPLE = 3 #: Quadruple Bond QUADRUPLE = 4 #: Dative Bond DATIVE = 5 #: Complex Bond COMPLEX = 6 #: Ionic Bond IONIC = 7 #: Unknown/Unspecified Connectivity UNKNOWN = 255 class CoordinateType(enum.IntEnum): """Coordinate Set Type Distinctions.""" #: 2D Coordinates TWO_D = 1 #: 3D Coordinates (should also indicate units, below) THREE_D = 2 #: Depositor Provided Coordinates SUBMITTED = 3 #: Experimentally Determined Coordinates EXPERIMENTAL = 4 #: Computed Coordinates COMPUTED = 5 #: Standardized Coordinates STANDARDIZED = 6 #: Hybrid Original with Computed Coordinates (e.g., explicit H) AUGMENTED = 7 #: Template used to align drawing ALIGNED = 8 #: Drawing uses shorthand forms (e.g., COOH, OCH3, Et, etc.) COMPACT = 9 #: (3D) Coordinate units are Angstroms UNITS_ANGSTROMS = 10 #: (3D) Coordinate units are nanometers UNITS_NANOMETERS = 11 #: (2D) Coordinate units are pixels UNITS_PIXEL = 12 #: (2D) Coordinate units are points UNITS_POINTS = 13 #: (2D) Coordinate units are standard bond lengths (1.0) UNITS_STDBONDS = 14 #: Coordinate units are unknown or unspecified UNITS_UNKNOWN = 255 class ProjectCategory(enum.IntEnum): """To distinguish projects funded through MLSCN, MLPCN or other.""" #: Assay depositions from MLSCN screen center MLSCN = 1 #: Assay depositions from MLPCN screen center MLPCN = 2 #: Assay depositions from MLSCN assay provider MLSCN_AP = 3 #: Assay depositions from MLPCN assay provider MLPCN_AP = 4 #: To be deprecated and replaced by options 7, 8 & 9 JOURNAL_ARTICLE = 5 #: Assay depositions from assay vendors ASSAY_VENDOR = 6 #: Data from literature, extracted by curators LITERATURE_EXTRACTED = 7 #: Data from literature, submitted by author of articles LITERATURE_AUTHOR = 8 #: Data from literature, submitted by journals/publishers LITERATURE_PUBLISHER = 9 #: RNAi screenings from RNAi Global Initiative RNAIGI = 10 #: Other project category OTHER = 255 #: Dictionary mapping atomic numbers to their element symbols. #: #: This dictionary includes 118 standard chemical elements from Hydrogen (1) to #: Oganesson (118), plus special atom types used by PubChem for non-standard entities #: like dummy atoms, R-group labels, and lone pairs. ELEMENTS: dict[int, str] = { # Standard chemical elements 1: "H", # Hydrogen 2: "He", # Helium 3: "Li", # Lithium 4: "Be", # Beryllium 5: "B", # Boron 6: "C", # Carbon 7: "N", # Nitrogen 8: "O", # Oxygen 9: "F", # Fluorine 10: "Ne", # Neon 11: "Na", # Sodium 12: "Mg", # Magnesium 13: "Al", # Aluminium 14: "Si", # Silicon 15: "P", # Phosphorus 16: "S", # Sulfur 17: "Cl", # Chlorine 18: "Ar", # Argon 19: "K", # Potassium 20: "Ca", # Calcium 21: "Sc", # Scandium 22: "Ti", # Titanium 23: "V", # Vanadium 24: "Cr", # Chromium 25: "Mn", # Manganese 26: "Fe", # Iron 27: "Co", # Cobalt 28: "Ni", # Nickel 29: "Cu", # Copper 30: "Zn", # Zinc 31: "Ga", # Gallium 32: "Ge", # Germanium 33: "As", # Arsenic 34: "Se", # Selenium 35: "Br", # Bromine 36: "Kr", # Krypton 37: "Rb", # Rubidium 38: "Sr", # Strontium 39: "Y", # Yttrium 40: "Zr", # Zirconium 41: "Nb", # Niobium 42: "Mo", # Molybdenum 43: "Tc", # Technetium 44: "Ru", # Ruthenium 45: "Rh", # Rhodium 46: "Pd", # Palladium 47: "Ag", # Silver 48: "Cd", # Cadmium 49: "In", # Indium 50: "Sn", # Tin 51: "Sb", # Antimony 52: "Te", # Tellurium 53: "I", # Iodine 54: "Xe", # Xenon 55: "Cs", # Cesium 56: "Ba", # Barium 57: "La", # Lanthanum 58: "Ce", # Cerium 59: "Pr", # Praseodymium 60: "Nd", # Neodymium 61: "Pm", # Promethium 62: "Sm", # Samarium 63: "Eu", # Europium 64: "Gd", # Gadolinium 65: "Tb", # Terbium 66: "Dy", # Dysprosium 67: "Ho", # Holmium 68: "Er", # Erbium 69: "Tm", # Thulium 70: "Yb", # Ytterbium 71: "Lu", # Lutetium 72: "Hf", # Hafnium 73: "Ta", # Tantalum 74: "W", # Tungsten 75: "Re", # Rhenium 76: "Os", # Osmium 77: "Ir", # Iridium 78: "Pt", # Platinum 79: "Au", # Gold 80: "Hg", # Mercury 81: "Tl", # Thallium 82: "Pb", # Lead 83: "Bi", # Bismuth 84: "Po", # Polonium 85: "At", # Astatine 86: "Rn", # Radon 87: "Fr", # Francium 88: "Ra", # Radium 89: "Ac", # Actinium 90: "Th", # Thorium 91: "Pa", # Protactinium 92: "U", # Uranium 93: "Np", # Neptunium 94: "Pu", # Plutonium 95: "Am", # Americium 96: "Cm", # Curium 97: "Bk", # Berkelium 98: "Cf", # Californium 99: "Es", # Einsteinium 100: "Fm", # Fermium 101: "Md", # Mendelevium 102: "No", # Nobelium 103: "Lr", # Lawrencium 104: "Rf", # Rutherfordium 105: "Db", # Dubnium 106: "Sg", # Seaborgium 107: "Bh", # Bohrium 108: "Hs", # Hassium 109: "Mt", # Meitnerium 110: "Ds", # Darmstadtium 111: "Rg", # Roentgenium 112: "Cn", # Copernicium 113: "Nh", # Nihonium 114: "Fl", # Flerovium 115: "Mc", # Moscovium 116: "Lv", # Livermorium 117: "Ts", # Tennessine 118: "Og", # Oganesson # Special atom types 252: "Lp", # Lone Pair 253: "R", # Rgroup Label 254: "*", # Dummy Atom 255: "*", # Unspecified Atom (Asterisk) } def request( identifier: str | int | list[str | int], namespace: str = "cid", domain: str = "compound", operation: str | None = None, output: str = "JSON", searchtype: str | None = None, **kwargs: QueryParam, ) -> HTTPResponse: """Construct API request from parameters and return the response. Full specification at https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest """ if not identifier: raise ValueError("identifier/cid cannot be None") # If identifier is a list, join with commas into string if isinstance(identifier, int): identifier = str(identifier) if not isinstance(identifier, str): identifier = ",".join(str(x) for x in identifier) # Filter None values from kwargs kwargs = {k: v for k, v in kwargs.items() if v is not None} # Build API URL urlid, postdata = None, None if namespace == "sourceid": identifier = identifier.replace("/", ".") if ( namespace in ["listkey", "formula", "sourceid"] or searchtype == "xref" or (searchtype and namespace == "cid") or domain == "sources" ): urlid = quote(identifier.encode("utf8")) else: postdata = urlencode([(namespace, identifier)]).encode("utf8") comps = filter( None, [API_BASE, domain, searchtype, namespace, urlid, operation, output] ) apiurl = "/".join(comps) if kwargs: apiurl += f"?{urlencode(kwargs)}" # Make request try: log.debug(f"Request URL: {apiurl}") log.debug(f"Request data: {postdata}") context = ssl.create_default_context(cafile=_CA_FILE) response = urlopen(apiurl, postdata, context=context) return response except HTTPError as e: raise create_http_error(e) from e def get( identifier: str | int | list[str | int], namespace: str = "cid", domain: str = "compound", operation: str | None = None, output: str = "JSON", searchtype: str | None = None, **kwargs: QueryParam, ) -> bytes: """Request wrapper that automatically handles async requests.""" if (searchtype and searchtype != "xref") or namespace in ["formula"]: response = request( identifier, namespace, domain, None, "JSON", searchtype, **kwargs ).read() status = json.loads(response.decode()) if "Waiting" in status and "ListKey" in status["Waiting"]: identifier = status["Waiting"]["ListKey"] namespace = "listkey" while "Waiting" in status and "ListKey" in status["Waiting"]: time.sleep(2) response = request( identifier, namespace, domain, operation, "JSON", **kwargs ).read() status = json.loads(response.decode()) if not output == "JSON": response = request( identifier, namespace, domain, operation, output, searchtype, **kwargs, ).read() else: response = request( identifier, namespace, domain, operation, output, searchtype, **kwargs ).read() return response def get_json( identifier: str | int | list[str | int], namespace: str = "cid", domain: str = "compound", operation: str | None = None, searchtype: str | None = None, **kwargs: QueryParam, ) -> dict[str, t.Any] | None: """Request wrapper that automatically parses JSON response into a python dict. This function suppresses NotFoundError and returns None if no results are found. """ try: return json.loads( get( identifier, namespace, domain, operation, "JSON", searchtype, **kwargs ).decode() ) except NotFoundError as e: log.info(e) return None def get_sdf( identifier: str | int | list[str | int], namespace: str = "cid", domain: str = "compound", operation: str | None = None, searchtype: str | None = None, **kwargs: QueryParam, ) -> str | None: """Request wrapper that automatically extracts SDF from the response. This function suppresses NotFoundError and returns None if no results are found. """ try: return get( identifier, namespace, domain, operation, "SDF", searchtype, **kwargs ).decode() except NotFoundError as e: log.info(e) return None def get_compounds( identifier: str | int | list[str | int], namespace: str = "cid", searchtype: str | None = None, as_dataframe: bool = False, **kwargs: QueryParam, ) -> list[Compound] | pd.DataFrame: """Retrieve the specified compound records from PubChem. Args: identifier: The compound identifier to use as a search query. namespace: The identifier type, one of cid, name, smiles, sdf, inchi, inchikey or formula. searchtype: The advanced search type, one of substructure, superstructure or similarity. as_dataframe: Automatically extract the Compound properties into a pandas DataFrame and return that. **kwargs: Additional query parameters to pass to the API request. Returns: List of :class:`~pubchempy.Compound` objects, or a pandas DataFrame if ``as_dataframe=True``. """ results = get_json(identifier, namespace, searchtype=searchtype, **kwargs) compounds = [Compound(r) for r in results["PC_Compounds"]] if results else [] if as_dataframe: return compounds_to_frame(compounds) return compounds def get_substances( identifier: str | int | list[str | int], namespace: str = "sid", as_dataframe: bool = False, **kwargs: QueryParam, ) -> list[Substance] | pd.DataFrame: """Retrieve the specified substance records from PubChem. Args: identifier: The substance identifier to use as a search query. namespace: The identifier type, one of sid, name or sourceid/. as_dataframe: Automatically extract the Substance properties into a pandas DataFrame and return that. **kwargs: Additional query parameters to pass to the API request. Returns: List of :class:`~pubchempy.Substance` objects, or a pandas DataFrame if ``as_dataframe=True``. """ results = get_json(identifier, namespace, "substance", **kwargs) substances = [Substance(r) for r in results["PC_Substances"]] if results else [] if as_dataframe: return substances_to_frame(substances) return substances def get_assays( identifier: str | int | list[str | int], namespace: str = "aid", **kwargs: QueryParam, ) -> list[Assay]: """Retrieve the specified assay records from PubChem. Args: identifier: The assay identifier to use as a search query. namespace: The identifier type. **kwargs: Additional query parameters to pass to the API request. Returns: List of :class:`~pubchempy.Assay` objects. """ results = get_json(identifier, namespace, "assay", "description", **kwargs) return [Assay(r) for r in results["PC_AssayContainer"]] if results else [] #: Dictionary mapping property names to their PubChem API equivalents. #: #: Allows properties to optionally be specified as underscore_separated, #: consistent with Compound attributes. PROPERTY_MAP: dict[str, str] = { "molecular_formula": "MolecularFormula", "molecular_weight": "MolecularWeight", "smiles": "SMILES", "connectivity_smiles": "ConnectivitySMILES", "canonical_smiles": "CanonicalSMILES", "isomeric_smiles": "IsomericSMILES", "inchi": "InChI", "inchikey": "InChIKey", "iupac_name": "IUPACName", "xlogp": "XLogP", "exact_mass": "ExactMass", "monoisotopic_mass": "MonoisotopicMass", "tpsa": "TPSA", "complexity": "Complexity", "charge": "Charge", "h_bond_donor_count": "HBondDonorCount", "h_bond_acceptor_count": "HBondAcceptorCount", "rotatable_bond_count": "RotatableBondCount", "heavy_atom_count": "HeavyAtomCount", "isotope_atom_count": "IsotopeAtomCount", "atom_stereo_count": "AtomStereoCount", "defined_atom_stereo_count": "DefinedAtomStereoCount", "undefined_atom_stereo_count": "UndefinedAtomStereoCount", "bond_stereo_count": "BondStereoCount", "defined_bond_stereo_count": "DefinedBondStereoCount", "undefined_bond_stereo_count": "UndefinedBondStereoCount", "covalent_unit_count": "CovalentUnitCount", "volume_3d": "Volume3D", "conformer_rmsd_3d": "ConformerModelRMSD3D", "conformer_model_rmsd_3d": "ConformerModelRMSD3D", "x_steric_quadrupole_3d": "XStericQuadrupole3D", "y_steric_quadrupole_3d": "YStericQuadrupole3D", "z_steric_quadrupole_3d": "ZStericQuadrupole3D", "feature_count_3d": "FeatureCount3D", "feature_acceptor_count_3d": "FeatureAcceptorCount3D", "feature_donor_count_3d": "FeatureDonorCount3D", "feature_anion_count_3d": "FeatureAnionCount3D", "feature_cation_count_3d": "FeatureCationCount3D", "feature_ring_count_3d": "FeatureRingCount3D", "feature_hydrophobe_count_3d": "FeatureHydrophobeCount3D", "effective_rotor_count_3d": "EffectiveRotorCount3D", "conformer_count_3d": "ConformerCount3D", } def get_properties( properties: str | list[str], identifier: str | int | list[str | int], namespace: str = "cid", searchtype: str | None = None, as_dataframe: bool = False, **kwargs: QueryParam, ) -> list[dict[str, t.Any]] | pd.DataFrame: """Retrieve the specified compound properties from PubChem. Args: properties: The properties to retrieve. identifier: The compound identifier to use as a search query. namespace: The identifier type. searchtype: The advanced search type, one of substructure, superstructure or similarity. as_dataframe: Automatically extract the properties into a pandas DataFrame. **kwargs: Additional query parameters to pass to the API request. """ if isinstance(properties, str): properties = properties.split(",") properties = ",".join([PROPERTY_MAP.get(p, p) for p in properties]) properties = f"property/{properties}" results = get_json( identifier, namespace, "compound", properties, searchtype=searchtype, **kwargs ) results = results["PropertyTable"]["Properties"] if results else [] if as_dataframe: import pandas as pd return pd.DataFrame.from_records(results, index="CID") return results def get_synonyms( identifier: str | int | list[str | int], namespace: str = "cid", domain: str = "compound", searchtype: str | None = None, **kwargs: QueryParam, ) -> list[dict[str, t.Any]]: """Retrieve synonyms (alternative names) for the specified records from PubChem. Synonyms include systematic names, common names, trade names, registry numbers, and other identifiers associated with compounds, substances, or assays. Args: identifier: The identifier to use as a search query. namespace: The identifier type (e.g., cid, name, smiles for compounds). domain: The PubChem domain to search (compound or substance). searchtype: The advanced search type, one of substructure, superstructure or similarity. **kwargs: Additional parameters to pass to the request. Returns: List of dictionaries containing synonym information for each matching record. Each dictionary contains the record identifier and a list of synonyms. """ results = get_json( identifier, namespace, domain, "synonyms", searchtype=searchtype, **kwargs ) return results["InformationList"]["Information"] if results else [] def get_cids( identifier: str | int | list[str | int], namespace: str = "name", domain: str = "compound", searchtype: str | None = None, **kwargs: QueryParam, ) -> list[int]: """Retrieve Compound Identifiers (CIDs) for the specified query from PubChem. CIDs are unique numerical identifiers assigned to each standardized compound record in the PubChem Compound database. This function is useful for converting between different identifier types (names, SMILES, InChI, etc.) and CIDs. Args: identifier: The identifier to use as a search query. namespace: The identifier type (e.g. name, smiles, inchi, formula). domain: The PubChem domain to search (compound, substance, or assay). searchtype: The advanced search type, one of substructure, superstructure or similarity. **kwargs: Additional parameters to pass to the request. Returns: List of CIDs (integers) that match the search criteria. Empty list if no matches found. """ results = get_json( identifier, namespace, domain, "cids", searchtype=searchtype, **kwargs ) if not results: return [] elif "IdentifierList" in results: return results["IdentifierList"]["CID"] elif "InformationList" in results: return results["InformationList"]["Information"] def get_sids( identifier: str | int | list[str | int], namespace: str = "cid", domain: str = "compound", searchtype: str | None = None, **kwargs: QueryParam, ) -> list[int]: """Retrieve Substance Identifiers (SIDs) for the specified query from PubChem. SIDs are unique numerical identifiers assigned to each substance record in the PubChem Substance database. This function is useful for finding which substance records are associated with a given compound or other identifier. Args: identifier: The identifier to use as a search query. namespace: The identifier type (e.g., cid, name, smiles for compounds). domain: The PubChem domain to search (compound, substance, or assay). searchtype: The advanced search type, one of substructure, superstructure or similarity. **kwargs: Additional parameters to pass to the request. Returns: List of SIDs (integers) that match the search criteria. Empty list if no matches found. """ results = get_json( identifier, namespace, domain, "sids", searchtype=searchtype, **kwargs ) if not results: return [] elif "IdentifierList" in results: return results["IdentifierList"]["SID"] elif "InformationList" in results: return results["InformationList"]["Information"] def get_aids( identifier: str | int | list[str | int], namespace: str = "cid", domain: str = "compound", searchtype: str | None = None, **kwargs: QueryParam, ) -> list[int]: """Retrieve Assay Identifiers (AIDs) for the specified query from PubChem. AIDs are unique numerical identifiers assigned to each biological assay record in the PubChem BioAssay database. This function is useful for finding which assays have tested a given compound or substance. Args: identifier: The identifier to use as a search query. namespace: The identifier type (e.g., cid, name, smiles). domain: The PubChem domain to search (compound, substance, or assay). searchtype: The advanced search type, one of substructure, superstructure or similarity. **kwargs: Additional parameters to pass to the request. Returns: List of AIDs (integers) that match the search criteria. Empty list if no matches found. """ results = get_json( identifier, namespace, domain, "aids", searchtype=searchtype, **kwargs ) if not results: return [] elif "IdentifierList" in results: return results["IdentifierList"]["AID"] elif "InformationList" in results: return results["InformationList"]["Information"] def get_all_sources(domain: str = "substance") -> list[str]: """Return a list of all current depositors of substances or assays.""" results = json.loads(get(domain, None, "sources").decode()) return results["InformationList"]["SourceName"] def download( outformat: str, path: str | os.PathLike, identifier: str | int | list[str | int], namespace: str = "cid", domain: str = "compound", operation: str | None = None, searchtype: str | None = None, overwrite: bool = False, **kwargs: QueryParam, ) -> None: """Format can be XML, ASNT/B, JSON, SDF, CSV, PNG, TXT.""" response = get( identifier, namespace, domain, operation, outformat, searchtype, **kwargs ) if not overwrite and os.path.isfile(path): raise OSError(f"{path} already exists. Use 'overwrite=True' to overwrite it.") with open(path, "wb") as f: f.write(response) def memoized_property(fget: t.Callable[[t.Any], t.Any]) -> property: """Decorator to create memoized properties. Used to cache :class:`~pubchempy.Compound` and :class:`~pubchempy.Substance` properties that require an additional request. """ attr_name = f"_{fget.__name__}" @functools.wraps(fget) def fget_memoized(self): if not hasattr(self, attr_name): setattr(self, attr_name, fget(self)) return getattr(self, attr_name) return property(fget_memoized) def deprecated(message: str) -> t.Callable[[t.Callable], t.Callable]: """Decorator to mark as deprecated and emit a warning when used.""" def deco(func): @functools.wraps(func) def wrapped(*args, **kwargs): warnings.warn( f"{func.__name__} is deprecated: {message}", category=PubChemPyDeprecationWarning, stacklevel=2, ) return func(*args, **kwargs) return wrapped return deco class Atom: """Class to represent an atom in a :class:`~pubchempy.Compound`.""" def __init__( self, aid: int, number: int, x: float | None = None, y: float | None = None, z: float | None = None, charge: int = 0, ) -> None: """Initialize with an atom ID, atomic number, coordinates and optional charge. Args: aid: Atom ID. number: Atomic number. x: X coordinate. y: Y coordinate. z: Z coordinate. charge: Formal charge on atom. """ self.aid = aid """The atom ID within the owning Compound.""" self.number = number """The atomic number for this atom.""" self.x = x """The x coordinate for this atom.""" self.y = y """The y coordinate for this atom.""" self.z = z """The z coordinate for this atom. Will be ``None`` in 2D Compound records.""" self.charge = charge """The formal charge on this atom.""" def __repr__(self) -> str: return f"Atom({self.aid!r}, {self.element!r})" def __eq__(self, other: object) -> bool: return ( isinstance(other, type(self)) and self.aid == other.aid and self.element == other.element and self.x == other.x and self.y == other.y and self.z == other.z and self.charge == other.charge ) @deprecated("Dictionary style access to Atom attributes is deprecated") def __getitem__(self, prop): """Allow dict-style access to attributes for backwards compatibility.""" if prop in {"element", "x", "y", "z", "charge"}: return getattr(self, prop) raise KeyError(prop) @deprecated("Dictionary style access to Atom attributes is deprecated") def __setitem__(self, prop, val): """Allow dict-style setting of attributes for backwards compatibility.""" setattr(self, prop, val) @deprecated("Dictionary style access to Atom attributes is deprecated") def __contains__(self, prop): """Allow dict-style checking of attributes for backwards compatibility.""" if prop in {"element", "x", "y", "z", "charge"}: return getattr(self, prop) is not None return False @property def element(self) -> str: """The element symbol for this atom.""" return ELEMENTS.get(self.number, str(self.number)) def to_dict(self) -> dict[str, t.Any]: """Return a dictionary containing Atom data.""" data = {"aid": self.aid, "number": self.number, "element": self.element} for coord in {"x", "y", "z"}: if getattr(self, coord) is not None: data[coord] = getattr(self, coord) if self.charge != 0: data["charge"] = self.charge return data def set_coordinates(self, x: float, y: float, z: float | None = None) -> None: """Set all coordinate dimensions at once.""" self.x = x self.y = y self.z = z @property def coordinate_type(self) -> str: """Whether this atom has 2D or 3D coordinates.""" return "2d" if self.z is None else "3d" class Bond: """Class to represent a bond between two atoms in a :class:`~pubchempy.Compound`.""" def __init__( self, aid1: int, aid2: int, order: BondType = BondType.SINGLE, style: int | None = None, ) -> None: """Initialize with begin and end atom IDs, bond order and bond style. Args: aid1: Begin atom ID. aid2: End atom ID. order: Bond order. style: Bond style annotation. """ self.aid1 = aid1 """ID of the begin atom of this bond.""" self.aid2 = aid2 """ID of the end atom of this bond.""" self.order = order """Bond order.""" self.style = style """Bond style annotation.""" def __repr__(self) -> str: return f"Bond({self.aid1!r}, {self.aid2!r}, {self.order!r})" def __eq__(self, other: object) -> bool: return ( isinstance(other, type(self)) and self.aid1 == other.aid1 and self.aid2 == other.aid2 and self.order == other.order and self.style == other.style ) @deprecated("Dictionary style access to Bond attributes is deprecated") def __getitem__(self, prop): """Allow dict-style access to attributes for backwards compatibility.""" if prop in {"order", "style"}: return getattr(self, prop) raise KeyError(prop) @deprecated("Dictionary style access to Bond attributes is deprecated") def __setitem__(self, prop, val): """Allow dict-style setting of attributes for backwards compatibility.""" setattr(self, prop, val) @deprecated("Dictionary style access to Bond attributes is deprecated") def __contains__(self, prop): """Allow dict-style checking of attributes for backwards compatibility.""" if prop in {"order", "style"}: return getattr(self, prop) is not None return False @deprecated("Dictionary style access to Bond attributes is deprecated") def __delitem__(self, prop): """Allow dict-style deletion of attributes for backwards compatibility.""" if not hasattr(self.__wrapped, prop): raise KeyError(prop) delattr(self.__wrapped, prop) def to_dict(self) -> dict[str, t.Any]: """Return a dictionary containing Bond data.""" data = {"aid1": self.aid1, "aid2": self.aid2, "order": self.order} if self.style is not None: data["style"] = self.style return data class Compound: """Represents a standardized chemical structure record from PubChem. The PubChem Compound database contains standardized and deduplicated chemical structures derived from the Substance database. Each Compound is uniquely identified by a CID (Compound Identifier) and represents a unique chemical structure with calculated properties, descriptors, and associated experimental data. Examples: >>> compound = Compound.from_cid(2244) # Aspirin >>> print(f"Formula: {compound.molecular_formula}") Formula: C9H8O4 >>> print(f"IUPAC: {compound.iupac_name}") IUPAC: 2-acetyloxybenzoic acid >>> print(f"MW: {compound.molecular_weight}") MW: 180.16 """ def __init__(self, record: dict[str, t.Any]) -> None: """Initialize a Compound with a record dict from the PubChem PUG REST service. Args: record: Compound record returned by the PubChem PUG REST service. Note: Most users will not need to instantiate a Compound instance directly from a record. The :meth:`from_cid()` class method and the :func:`~get_compounds()` function offer more convenient ways to obtain Compound instances, as they also handle the retrieval of the record from PubChem. """ self._record = None self._atoms = {} self._bonds = {} self.record = record def _setup_atoms(self) -> None: """Derive Atom objects from the record.""" # Delete existing atoms self._atoms = {} # Create atoms aids = self.record["atoms"]["aid"] elements = self.record["atoms"]["element"] if not len(aids) == len(elements): raise ResponseParseError("Error parsing atom elements") for aid, element in zip(aids, elements): self._atoms[aid] = Atom(aid=aid, number=element) # Add coordinates if "coords" in self.record: coord_ids = self.record["coords"][0]["aid"] xs = self.record["coords"][0]["conformers"][0]["x"] ys = self.record["coords"][0]["conformers"][0]["y"] zs = self.record["coords"][0]["conformers"][0].get("z", []) if not len(coord_ids) == len(xs) == len(ys) == len(self._atoms) or ( zs and not len(zs) == len(coord_ids) ): raise ResponseParseError("Error parsing atom coordinates") for aid, x, y, z in zip_longest(coord_ids, xs, ys, zs): self._atoms[aid].set_coordinates(x, y, z) # Add charges if "charge" in self.record["atoms"]: for charge in self.record["atoms"]["charge"]: self._atoms[charge["aid"]].charge = charge["value"] def _setup_bonds(self) -> None: """Derive Bond objects from the record.""" self._bonds = {} if "bonds" not in self.record: return # Create bonds aid1s = self.record["bonds"]["aid1"] aid2s = self.record["bonds"]["aid2"] orders = self.record["bonds"]["order"] if not len(aid1s) == len(aid2s) == len(orders): raise ResponseParseError("Error parsing bonds") for aid1, aid2, order in zip(aid1s, aid2s, orders): self._bonds[frozenset((aid1, aid2))] = Bond( aid1=aid1, aid2=aid2, order=order ) # Add styles if ( "coords" in self.record and "style" in self.record["coords"][0]["conformers"][0] ): aid1s = self.record["coords"][0]["conformers"][0]["style"]["aid1"] aid2s = self.record["coords"][0]["conformers"][0]["style"]["aid2"] styles = self.record["coords"][0]["conformers"][0]["style"]["annotation"] for aid1, aid2, style in zip(aid1s, aid2s, styles): self._bonds[frozenset((aid1, aid2))].style = style @classmethod def from_cid(cls, cid: int, **kwargs: QueryParam) -> Compound: """Retrieve the Compound record for the specified CID. Args: cid: The PubChem Compound Identifier (CID) to retrieve. **kwargs: Additional parameters to pass to the request. Example: c = Compound.from_cid(6819) """ record = json.loads(request(cid, **kwargs).read().decode())["PC_Compounds"][0] return cls(record) @property def record(self) -> dict[str, t.Any]: """The full compound record returned by the PubChem PUG REST service.""" return self._record @record.setter def record(self, record: dict[str, t.Any]) -> None: self._record = record log.debug(f"Created {self}") self._setup_atoms() self._setup_bonds() def __repr__(self) -> str: return f"Compound({self.cid if self.cid else ''})" def __eq__(self, other: object) -> bool: return isinstance(other, type(self)) and self.record == other.record def to_dict(self, properties: list[str] | None = None) -> dict[str, t.Any]: """Return a dict containing Compound property data. Optionally specify a list of the desired properties to include. If ``properties`` is not specified, all properties are included, with the following exceptions: :attr:`synonyms`, :attr:`aids` and :attr:`sids` are not included unless explicitly specified. This is because they each require an extra request to the PubChem API to retrieve. :attr:`canonical_smiles` and :attr:`isomeric_smiles` are not included by default, as they are deprecated and have been replaced by :attr:`connectivity_smiles` and :attr:`smiles` respectively. Args: properties: List of desired properties. Returns: Dictionary of compound data. """ if not properties: skip = { "record", "aids", "sids", "synonyms", "canonical_smiles", "isomeric_smiles", } properties = [ p for p, v in Compound.__dict__.items() if isinstance(v, property) and p not in skip ] return { p: [i.to_dict() for i in getattr(self, p)] if p in {"atoms", "bonds"} else getattr(self, p) for p in properties } def to_series(self, properties: list[str] | None = None) -> pd.Series: """Return a pandas :class:`~pandas.Series` containing Compound data. Optionally specify a list of the desired properties to include as columns. If ``properties`` is not specified, all properties are included, with the following exceptions: :attr:`synonyms`, :attr:`aids` and :attr:`sids` are not included unless explicitly specified. This is because they each require an extra request to the PubChem API to retrieve. :attr:`canonical_smiles` and :attr:`isomeric_smiles` are not included by default, as they are deprecated and have been replaced by :attr:`connectivity_smiles` and :attr:`smiles` respectively. Args: properties: List of desired properties. """ import pandas as pd return pd.Series(self.to_dict(properties)) @property def cid(self) -> int | None: """The PubChem Compound Identifier (CID). .. note:: When searching using a SMILES or InChI query that is not present in the PubChem Compound database, an automatically generated record may be returned that contains properties that have been calculated on the fly. These records will not have a CID property. """ try: return self.record["id"]["id"]["cid"] except KeyError: return None @property def elements(self) -> list[str]: """List of element symbols for atoms in this Compound.""" return [a.element for a in self.atoms] @property def atoms(self) -> list[Atom]: """List of :class:`Atoms ` in this Compound.""" return sorted(self._atoms.values(), key=lambda x: x.aid) @property def bonds(self) -> list[Bond]: """List of :class:`Bonds ` in this Compound.""" return sorted(self._bonds.values(), key=lambda x: (x.aid1, x.aid2)) @memoized_property def synonyms(self) -> list[str] | None: """Ranked list of all the names associated with this Compound. Requires an extra request. Result is cached. """ if self.cid: results = get_json(self.cid, operation="synonyms") return ( results["InformationList"]["Information"][0]["Synonym"] if results else [] ) @memoized_property def sids(self) -> list[int] | None: """List of Substance Identifiers associated with this Compound. Requires an extra request. Result is cached. """ if self.cid: results = get_json(self.cid, operation="sids") return ( results["InformationList"]["Information"][0]["SID"] if results else [] ) @memoized_property def aids(self) -> list[int] | None: """List of Assay Identifiers associated with this Compound. Requires an extra request. Result is cached. """ if self.cid: results = get_json(self.cid, operation="aids") return ( results["InformationList"]["Information"][0]["AID"] if results else [] ) @property def coordinate_type(self) -> str | None: """Whether this Compound has 2D or 3D coordinates.""" if CoordinateType.TWO_D in self.record["coords"][0]["type"]: return "2d" elif CoordinateType.THREE_D in self.record["coords"][0]["type"]: return "3d" @property def charge(self) -> int: """Formal charge on this Compound.""" return self.record["charge"] if "charge" in self.record else 0 @property def molecular_formula(self) -> str | None: """Molecular formula. The molecular formula represents the number of atoms of each element in a compound. It does not contain any information about connectivity or structure. """ return _parse_prop({"label": "Molecular Formula"}, self.record["props"]) @property def molecular_weight(self) -> float | None: """Molecular weight in g/mol. The molecular weight is the sum of all atomic weights of the constituent atoms in a compound, measured in g/mol. In the absence of explicit isotope labelling, averaged natural abundance is assumed. If an atom bears an explicit isotope label, 100% isotopic purity is assumed at this location. """ sval = _parse_prop({"label": "Molecular Weight"}, self.record["props"]) return float(sval) if sval else None @property @deprecated("Use connectivity_smiles instead") def canonical_smiles(self) -> str | None: """Canonical SMILES, with no stereochemistry information (deprecated). .. deprecated:: 1.0.5 :attr:`canonical_smiles` is deprecated, use :attr:`connectivity_smiles` instead. """ return self.connectivity_smiles @property @deprecated("Use smiles instead") def isomeric_smiles(self) -> str | None: """Isomeric SMILES. .. deprecated:: 1.0.5 :attr:`isomeric_smiles` is deprecated, use :attr:`smiles` instead. """ return self.smiles @property def connectivity_smiles(self) -> str | None: """Connectivity SMILES string. A canonical SMILES string that includes connectivity information only. It excludes stereochemical and isotopic information. Replaces the deprecated :attr:`canonical_smiles` property. """ return _parse_prop( {"label": "SMILES", "name": "Connectivity"}, self.record["props"] ) @property def smiles(self) -> str | None: """Absolute SMILES string (isomeric and canonical). A canonical SMILES string that includes both stereochemical and isotopic information. This provides the most complete linear representation of the molecular structure. Replaces the deprecated :attr:`isomeric_smiles` property. """ return _parse_prop( {"label": "SMILES", "name": "Absolute"}, self.record["props"] ) @property def inchi(self) -> str | None: """Standard IUPAC International Chemical Identifier (InChI). The InChI provides a unique, standardized representation of molecular structure that is not dependent on the software used to generate it. It includes connectivity, stereochemistry, and isotopic information in a layered format. This standard version does not allow for user selectable options in dealing with stereochemistry and tautomer layers. """ return _parse_prop({"label": "InChI", "name": "Standard"}, self.record["props"]) @property def inchikey(self) -> str | None: """Standard InChIKey. A hashed version of the full standard InChI, consisting of 27 characters divided into three blocks separated by hyphens. The InChIKey provides a fixed-length identifier that is more suitable for database indexing and web searches than the full InChI string. """ return _parse_prop( {"label": "InChIKey", "name": "Standard"}, self.record["props"] ) @property def iupac_name(self) -> str | None: """Preferred IUPAC name. The chemical name systematically determined according to IUPAC (International Union of Pure and Applied Chemistry) nomenclature rules. This is the preferred systematic name among the available IUPAC naming styles (Allowed, CAS-like Style, Preferred, Systematic, Traditional). """ # Note: record has Allowed, CAS-like Style, Preferred, Systematic, Traditional return _parse_prop( {"label": "IUPAC Name", "name": "Preferred"}, self.record["props"] ) @property def xlogp(self) -> float | None: """XLogP octanol-water partition coefficient. A computationally generated octanol-water partition coefficient that measures the hydrophilicity or hydrophobicity of a molecule. Higher values indicate more lipophilic (fat-soluble) compounds, while lower values indicate more hydrophilic (water-soluble) compounds. """ return _parse_prop({"label": "Log P"}, self.record["props"]) @property def exact_mass(self) -> float | None: """Exact mass in Da (Daltons). The mass of the most likely isotopic composition for a single molecule, corresponding to the most intense ion/molecule peak in a mass spectrum. This differs from molecular weight in that it uses the exact masses of specific isotopes rather than averaged atomic weights. """ sval = _parse_prop({"label": "Mass", "name": "Exact"}, self.record["props"]) return float(sval) if sval else None @property def monoisotopic_mass(self) -> float | None: """Monoisotopic mass in Da (Daltons). The mass of a molecule calculated using the mass of the most abundant isotope of each element. This provides a single, well-defined mass value useful for high-resolution mass spectrometry applications. """ sval = _parse_prop( {"label": "Weight", "name": "MonoIsotopic"}, self.record["props"] ) return float(sval) if sval else None @property def tpsa(self) -> float | None: """Topological Polar Surface Area (TPSA). The topological polar surface area computed using the algorithm described by Ertl et al. TPSA is a commonly used descriptor for predicting drug absorption, as it correlates well with passive molecular transport through membranes. Values are typically expressed in square Ångströms. """ return _parse_prop({"implementation": "E_TPSA"}, self.record["props"]) @property def complexity(self) -> float | None: """Molecular complexity rating. A measure of molecular complexity computed using the Bertz/Hendrickson/ Ihlenfeldt formula. This descriptor quantifies the structural complexity of a molecule based on factors such as the number of atoms, bonds, rings, and branching patterns. """ return _parse_prop({"implementation": "E_COMPLEXITY"}, self.record["props"]) @property def h_bond_donor_count(self) -> int | None: """Number of hydrogen-bond donors in the structure. Counts functional groups that can donate hydrogen bonds, such as -OH, -NH, and -SH groups. This descriptor is important for predicting drug-like properties and molecular interactions. """ return _parse_prop({"implementation": "E_NHDONORS"}, self.record["props"]) @property def h_bond_acceptor_count(self) -> int | None: """Number of hydrogen-bond acceptors in the structure. Counts functional groups that can accept hydrogen bonds, such as oxygen and nitrogen atoms with lone pairs. This descriptor is important for predicting drug-like properties and molecular interactions. """ return _parse_prop({"implementation": "E_NHACCEPTORS"}, self.record["props"]) @property def rotatable_bond_count(self) -> int | None: """Number of rotatable bonds. Counts single bonds that can freely rotate, excluding bonds in rings and terminal bonds to hydrogen or methyl groups. """ return _parse_prop({"implementation": "E_NROTBONDS"}, self.record["props"]) @property def fingerprint(self) -> str | None: """Raw padded and hex-encoded structural fingerprint from PubChem. Returns the raw padded and hex-encoded fingerprint as returned by the PUG REST API. This is the underlying data used to generate the human-readable binary fingerprint via the ``cactvs_fingerprint`` property. Most users should use ``cactvs_fingerprint`` instead for substructure analysis and similarity calculations. The PubChem fingerprint data is 881 bits in length. Binary data is stored in one byte increments. This fingerprint is, therefore, 111 bytes in length (888 bits), which includes padding of seven bits at the end to complete the last byte. A four-byte prefix, containing the bit length of the fingerprint (881 bits), increases the stored PubChem fingerprint size to 115 bytes (920 bits). This is then hex-encoded, resulting in a 230-character string. More information at: ftp://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_fingerprints.txt """ return _parse_prop({"implementation": "E_SCREEN"}, self.record["props"]) @property def cactvs_fingerprint(self) -> str | None: """PubChem CACTVS structural fingerprint as 881-bit binary string. Returns a binary fingerprint string where each character is a bit representing the presence (1) or absence (0) of specific chemical substructures and features. The 881-bit fingerprint is organized into sections covering: - Section 1: Hierarchical element counts (1-115) - Section 2: Rings in a canonical ring set (116-163) - Section 3: Simple atom pairs (164-218) - Section 4: Simple atom nearest neighbors (219-242) - Section 5: Detailed atom neighborhoods (243-707) - Section 6: Simple SMARTS patterns (708-881) This fingerprint enables efficient substructure searching, similarity calculations, and chemical clustering. More information at: ftp://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_fingerprints.txt """ # Skip first 4 bytes (contain length of fingerprint) and last 7 bits (padding) # then re-pad to 881 bits return f"{int(self.fingerprint[8:], 16):020b}"[:-7].zfill(881) @property def heavy_atom_count(self) -> int | None: """Number of heavy atoms (non-hydrogen atoms). Counts all atoms in the molecule except hydrogen. This is a basic descriptor of molecular size and is used in various chemical calculations and molecular property predictions. """ if "count" in self.record and "heavy_atom" in self.record["count"]: return self.record["count"]["heavy_atom"] @property def isotope_atom_count(self) -> int | None: """Number of atoms with enriched isotopes. Counts atoms that are specified with non-standard isotopes (e.g., ²H, ¹³C). Most organic molecules have a value of 0 unless they are isotopically labeled for research or analytical purposes. """ if "count" in self.record and "isotope_atom" in self.record["count"]: return self.record["count"]["isotope_atom"] @property def atom_stereo_count(self) -> int | None: """Total number of atoms with tetrahedral (sp³) stereochemistry. Counts atoms that have tetrahedral stereochemistry. This includes both defined and undefined stereocenters in the molecule. """ if "count" in self.record and "atom_chiral" in self.record["count"]: return self.record["count"]["atom_chiral"] @property def defined_atom_stereo_count(self) -> int | None: """Number of atoms with defined tetrahedral (sp³) stereochemistry. Counts stereocenters where the absolute configuration is explicitly specified (e.g. R or S). This excludes stereocenters where the configuration is unknown or unspecified. """ if "count" in self.record and "atom_chiral_def" in self.record["count"]: return self.record["count"]["atom_chiral_def"] @property def undefined_atom_stereo_count(self) -> int | None: """Number of atoms with undefined tetrahedral (sp³) stereochemistry. Counts stereocenters where the absolute configuration is not specified or is unknown. These represent potential stereocenters that could have either R or S configuration, but this is not explicitly defined. """ if "count" in self.record and "atom_chiral_undef" in self.record["count"]: return self.record["count"]["atom_chiral_undef"] @property def bond_stereo_count(self) -> int | None: """Bond stereocenter count.""" if "count" in self.record and "bond_chiral" in self.record["count"]: return self.record["count"]["bond_chiral"] @property def defined_bond_stereo_count(self) -> int | None: """Defined bond stereocenter count.""" if "count" in self.record and "bond_chiral_def" in self.record["count"]: return self.record["count"]["bond_chiral_def"] @property def undefined_bond_stereo_count(self) -> int | None: """Undefined bond stereocenter count.""" if "count" in self.record and "bond_chiral_undef" in self.record["count"]: return self.record["count"]["bond_chiral_undef"] @property def covalent_unit_count(self) -> int | None: """Covalently-bonded unit count.""" if "count" in self.record and "covalent_unit" in self.record["count"]: return self.record["count"]["covalent_unit"] @property def volume_3d(self) -> float | None: """Analytic volume of the first diverse conformer. The 3D molecular volume calculated for the default (first diverse) conformer. This descriptor provides information about the space occupied by the molecule in three dimensions. """ conf = self.record["coords"][0]["conformers"][0] if "data" in conf: return _parse_prop({"label": "Shape", "name": "Volume"}, conf["data"]) @property def multipoles_3d(self) -> list[float] | None: conf = self.record["coords"][0]["conformers"][0] if "data" in conf: return _parse_prop({"label": "Shape", "name": "Multipoles"}, conf["data"]) @property def conformer_rmsd_3d(self) -> float | None: """Conformer sampling RMSD in Å. The root-mean-square deviation of atomic positions between different conformers in the conformer model. This measures the structural diversity of the generated conformer ensemble. """ coords = self.record["coords"][0] if "data" in coords: return _parse_prop({"label": "Conformer", "name": "RMSD"}, coords["data"]) @property def effective_rotor_count_3d(self) -> int | None: """Number of effective rotors in the 3D structure. A count of rotatable bonds that significantly contribute to conformational flexibility. This is often less than the total rotatable bond count as it excludes rotors that have restricted rotation due to steric or electronic effects. """ return _parse_prop( {"label": "Count", "name": "Effective Rotor"}, self.record["props"] ) @property def pharmacophore_features_3d(self) -> list[str] | None: """3D pharmacophore features present in the molecule. A list of pharmacophore feature types identified in the 3D structure, such as hydrogen bond donors, acceptors, aromatic rings, and hydrophobic regions. These features are important for drug-target interactions. """ return _parse_prop( {"label": "Features", "name": "Pharmacophore"}, self.record["props"] ) @property def mmff94_partial_charges_3d(self) -> list[str] | None: return _parse_prop( {"label": "Charge", "name": "MMFF94 Partial"}, self.record["props"] ) @property def mmff94_energy_3d(self) -> float | None: conf = self.record["coords"][0]["conformers"][0] if "data" in conf: return _parse_prop( {"label": "Energy", "name": "MMFF94 NoEstat"}, conf["data"] ) @property def conformer_id_3d(self) -> str | None: conf = self.record["coords"][0]["conformers"][0] if "data" in conf: return _parse_prop({"label": "Conformer", "name": "ID"}, conf["data"]) @property def shape_selfoverlap_3d(self) -> float | None: conf = self.record["coords"][0]["conformers"][0] if "data" in conf: return _parse_prop({"label": "Shape", "name": "Self Overlap"}, conf["data"]) @property def feature_selfoverlap_3d(self) -> float | None: conf = self.record["coords"][0]["conformers"][0] if "data" in conf: return _parse_prop( {"label": "Feature", "name": "Self Overlap"}, conf["data"] ) @property def shape_fingerprint_3d(self) -> list[str] | None: conf = self.record["coords"][0]["conformers"][0] if "data" in conf: return _parse_prop({"label": "Fingerprint", "name": "Shape"}, conf["data"]) def _parse_prop(search: dict[str, str], proplist: list[dict[str, t.Any]]) -> t.Any: """Extract property value from record using the given urn search filter.""" props = [ i for i in proplist if all(item in i["urn"].items() for item in search.items()) ] if len(props) > 0: return props[0]["value"][list(props[0]["value"].keys())[0]] class Substance: """Represents a raw chemical record as originally deposited to PubChem. The PubChem Substance database contains chemical records in their original deposited form, before standardization or processing. As a result, it contains duplicates, mixtures, and some records that don't make chemical sense. This means that Substance records contain fewer calculated properties, however they do have additional information about the original source that deposited the record. During PubChem's standardization process, Substances are processed to create standardized Compound records. Multiple Substances may map to the same Compound if they represent the same unique chemical structure. Some Substances may not map to any Compound if they cannot be standardized. Examples: >>> substance = Substance.from_sid(12345) >>> print(f"Source: {substance.source_name}") Source: KEGG >>> print(f"Depositor ID: {substance.source_id}") Depositor ID: C10159 >>> print(f"Standardized to CID: {substance.standardized_cid}") Standardized to CID: 169683 """ def __init__(self, record: dict[str, t.Any]) -> None: """Initialize a Substance with a record dict from the PubChem PUG REST service. Args: record: Substance record returned by the PubChem PUG REST service. Note: Most users will not need to instantiate a Substance instance directly from a record. The :meth:`from_sid()` class method and the :func:`~get_substances()` function offer more convenient ways to obtain Substance instances, as they also handle the retrieval of the record from PubChem. """ self._record = record @classmethod def from_sid(cls, sid: int, **kwargs: QueryParam) -> Substance: """Retrieve the Substance record for the specified SID. Args: sid: The PubChem Substance Identifier (SID). **kwargs: Additional parameters to pass to the request. Example: s = Substance.from_sid(12345) """ response = request(sid, "sid", "substance", **kwargs).read().decode() record = json.loads(response)["PC_Substances"][0] return cls(record) @property def record(self) -> dict[str, t.Any]: """The full substance record returned by the PubChem PUG REST service.""" return self._record def __repr__(self) -> str: return f"Substance({self.sid if self.sid else ''})" def __eq__(self, other: object) -> bool: return isinstance(other, type(self)) and self.record == other.record def to_dict(self, properties: list[str] | None = None) -> dict[str, t.Any]: """Return a dict containing Substance property data. Optionally specify a list of the desired properties to include. If ``properties`` is not specified, all properties are included, with the following exceptions: :attr:`cids` and :attr:`aids` are not included unless explicitly specified. This is because they each require an extra request to the PubChem API to retrieve. Args: properties: List of desired properties. Returns: Dictionary of substance data. """ if not properties: skip = { "record", "deposited_compound", "standardized_compound", "cids", "aids", } properties = [ p for p, v in Substance.__dict__.items() if isinstance(v, property) and p not in skip ] return {p: getattr(self, p) for p in properties} def to_series(self, properties: list[str] | None = None) -> pd.Series: """Return a pandas :class:`~pandas.Series` containing Substance data. Optionally specify a list of the desired properties to include as columns. If ``properties`` is not specified, all properties are included, with the following exceptions: :attr:`cids` and :attr:`aids` are not included unless explicitly specified. This is because they each require an extra request to the PubChem API to retrieve. Args: properties: List of desired properties. """ import pandas as pd return pd.Series(self.to_dict(properties)) @property def sid(self) -> int: """The PubChem Substance Idenfitier (SID).""" return self.record["sid"]["id"] @property def synonyms(self) -> list[str] | None: """A ranked list of all the names associated with this Substance.""" if "synonyms" in self.record: return self.record["synonyms"] @property def source_name(self) -> str: """The name of the PubChem depositor that was the source of this Substance.""" return self.record["source"]["db"]["name"] @property def source_id(self) -> str: """Unique ID for this Substance from the PubChem depositor source.""" return self.record["source"]["db"]["source_id"]["str"] @property def standardized_cid(self) -> int | None: """The CID of the Compound that was standardized from this Substance. May not exist if this Substance was not standardizable. """ for c in self.record.get("compound", []): if c["id"]["type"] == CompoundIdType.STANDARDIZED: return c["id"]["id"]["cid"] @memoized_property def standardized_compound(self) -> Compound | None: """The :class:`~pubchempy.Compound` that was standardized from this Substance. Requires an extra request. Result is cached. May not exist if this Substance was not standardizable. """ cid = self.standardized_cid if cid: return Compound.from_cid(cid) @property def deposited_compound(self) -> Compound | None: """A :class:`~pubchempy.Compound` derived from the unstandardized Substance. This :class:`~pubchempy.Compound` is produced from the unstandardized Substance record as deposited. It will not have a ``cid`` and will be missing most properties. """ for c in self.record.get("compound", []): if c["id"]["type"] == CompoundIdType.DEPOSITED: return Compound(c) @memoized_property def cids(self) -> list[int]: """A list of all CIDs for Compounds that were standardized from this Substance. Requires an extra request. Result is cached. """ results = get_json(self.sid, "sid", "substance", "cids") return results["InformationList"]["Information"][0]["CID"] if results else [] @memoized_property def aids(self) -> list[int]: """A list of all AIDs for Assays associated with this Substance. Requires an extra request. Result is cached. """ results = get_json(self.sid, "sid", "substance", "aids") return results["InformationList"]["Information"][0]["AID"] if results else [] class Assay: """Represents a biological assay record from the PubChem BioAssay database. The PubChem BioAssay database contains experimental data from biological screening and testing programs. Each assay record describes the experimental conditions, methodology, and results for testing chemical compounds against biological targets. BioAssay records include: - Assay protocol and experimental conditions - Target information (proteins, genes, pathways) - Activity outcome definitions and thresholds - Results data linking compounds to biological activities - Source information and literature references Assays are identified by their AID (Assay Identifier) and can be retrieved using the ``from_aid()`` class method. The assay data provides the experimental context for understanding compound bioactivity data stored in PubChem. """ def __init__(self, record: dict[str, t.Any]) -> None: """Initialize an Assay with a record dict from the PubChem PUG REST service. Args: record: Assay record returned by the PubChem PUG REST service. Note: Most users will not need to instantiate an Assay instance directly from a record. The :meth:`from_aid()` class method and the :func:`~get_assays()` function offer more convenient ways to obtain Assay instances, as they also handle the retrieval of the record from PubChem. """ self._record = record @classmethod def from_aid(cls, aid: int, **kwargs: QueryParam) -> Assay: """Retrieve the Assay record for the specified AID. Args: aid: The PubChem Assay Identifier (AID). **kwargs: Additional parameters to pass to the request. Example: a = Assay.from_aid(1234) """ response = request(aid, "aid", "assay", "description", **kwargs).read().decode() record = json.loads(response)["PC_AssayContainer"][0] return cls(record) @property def record(self) -> dict[str, t.Any]: """The full assay record returned by the PubChem PUG REST service.""" return self._record def __repr__(self) -> str: return f"Assay({self.aid if self.aid else ''})" def __eq__(self, other: object) -> bool: return isinstance(other, type(self)) and self.record == other.record def to_dict(self, properties: list[str] | None = None) -> dict[str, t.Any]: """Return a dict containing Assay property data. Optionally specify a list of the desired properties to include. If ``properties`` is not specified, all properties are included. Args: properties: List of desired properties. Returns: Dictionary of assay data. """ if not properties: skip = {"record"} properties = [ p for p, v in Assay.__dict__.items() if isinstance(v, property) and p not in skip ] return {p: getattr(self, p) for p in properties} @property def aid(self) -> int: """The PubChem Assay Idenfitier (AID).""" return self.record["assay"]["descr"]["aid"]["id"] @property def name(self) -> str: """The short assay name, used for display purposes.""" return self.record["assay"]["descr"]["name"] @property def description(self) -> str: """Description.""" return self.record["assay"]["descr"]["description"] @property def project_category(self) -> ProjectCategory | None: """Category to distinguish projects funded through MLSCN, MLPCN or other. Possible values include mlscn, mlpcn, mlscn-ap, mlpcn-ap, literature-extracted, literature-author, literature-publisher, rnaigi. """ if "project_category" in self.record["assay"]["descr"]: return ProjectCategory(self.record["assay"]["descr"]["project_category"]) @property def comments(self) -> list[str]: """Comments and additional information.""" return [ comment for comment in self.record["assay"]["descr"]["comment"] if comment ] @property def results(self) -> list[dict[str, t.Any]]: """A list of dictionaries containing details of the results from this Assay.""" return self.record["assay"]["descr"]["results"] @property def target(self) -> list[dict[str, t.Any]] | None: """A list of dictionaries containing details of the Assay targets.""" if "target" in self.record["assay"]["descr"]: return self.record["assay"]["descr"]["target"] @property def revision(self) -> int: """Revision identifier for textual description.""" return self.record["assay"]["descr"]["revision"] @property def aid_version(self) -> int: """Incremented when the original depositor updates the record.""" return self.record["assay"]["descr"]["aid"]["version"] def compounds_to_frame( compounds: list[Compound] | Compound, properties: list[str] | None = None ) -> pd.DataFrame: """Create a :class:`~pandas.DataFrame` from a :class:`~pubchempy.Compound` list. Optionally specify the desired :class:`~pubchempy.Compound` properties to include as columns in the pandas DataFrame. """ import pandas as pd if isinstance(compounds, Compound): compounds = [compounds] properties = set(properties) | {"cid"} if properties else None return pd.DataFrame.from_records( [c.to_dict(properties) for c in compounds], index="cid" ) def substances_to_frame( substances: list[Substance] | Substance, properties: list[str] | None = None ) -> pd.DataFrame: """Create a :class:`~pandas.DataFrame` from a :class:`~pubchempy.Substance` list. Optionally specify a list of the desired :class:`~pubchempy.Substance` properties to include as columns in the pandas DataFrame. """ import pandas as pd if isinstance(substances, Substance): substances = [substances] properties = set(properties) | {"sid"} if properties else None return pd.DataFrame.from_records( [s.to_dict(properties) for s in substances], index="sid" ) class PubChemPyDeprecationWarning(Warning): """Warning category for deprecated features.""" class PubChemPyError(Exception): """Base class for all PubChemPy exceptions.""" class ResponseParseError(PubChemPyError): """PubChem response is uninterpretable.""" class PubChemHTTPError(PubChemPyError): """Generic error class to handle HTTP error codes.""" def __init__(self, code: int, msg: str, details: list[str]) -> None: """Initialize with HTTP status code, message, and additional details. Args: code: HTTP status code. msg: Error message. details: Additional error details from PubChem API. """ super().__init__(msg) self.code = code self.msg = msg self.details = details def __str__(self) -> str: output = f"PubChem HTTP Error {self.code} {self.msg}" if self.details: details = ", ".join(self.details) output = f"{output} ({details})" return output def __repr__(self) -> str: return ( f"{self.__class__.__name__}({self.code!r}, {self.msg!r}, {self.details!r})" ) def create_http_error(e: HTTPError) -> PubChemHTTPError: """Create appropriate PubChem HTTP error subclass based on status code.""" code = e.code msg = e.msg details = [] try: fault = json.loads(e.read().decode())["Fault"] msg = fault.get("Code", msg) if "Message" in fault: msg = f"{msg}: {fault['Message']}" details = fault.get("Details", []) except (ValueError, IndexError, KeyError): pass error_map = { 400: BadRequestError, 404: NotFoundError, 405: MethodNotAllowedError, 500: ServerError, 501: UnimplementedError, 503: ServerBusyError, 504: TimeoutError, } error_class = error_map.get(code, PubChemHTTPError) return error_class(code, msg, details) class BadRequestError(PubChemHTTPError): """400: Request is improperly formed (e.g. syntax error in the URL or POST body).""" class NotFoundError(PubChemHTTPError): """404: The input record was not found (e.g. invalid CID).""" class MethodNotAllowedError(PubChemHTTPError): """405: Request not allowed (e.g. invalid MIME type in the HTTP Accept header).""" class ServerError(PubChemHTTPError): """500: Some problem on the server side (e.g. a database server down).""" class UnimplementedError(PubChemHTTPError): """501: The requested operation has not (yet) been implemented by the server.""" class ServerBusyError(PubChemHTTPError): """503: Too many requests or server is busy, retry later.""" class TimeoutError(PubChemHTTPError): """504: The request timed out, from server overload or too broad a request. See :ref:`Avoiding TimeoutError ` for more information. """ if __name__ == "__main__": print(__version__) mcs07-PubChemPy-1fcc2e5/pyproject.toml000066400000000000000000000050631505763656600177320ustar00rootroot00000000000000[project] name = "PubChemPy" version = "1.0.5" description = "A simple Python wrapper around the PubChem PUG REST API." readme = "README.md" authors = [{ name = "Matt Swain", email = "m.swain@me.com" }] license = "MIT" license-files = ["LICENSE"] requires-python = ">=3.10" dependencies = [] keywords = ["pubchem", "python", "rest", "api", "chemistry", "cheminformatics"] classifiers = [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Healthcare Industry", "Intended Audience :: Science/Research", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Topic :: Database :: Front-Ends", "Topic :: Internet :: WWW/HTTP :: Dynamic Content", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Scientific/Engineering :: Chemistry", "Topic :: Scientific/Engineering :: Medical Science Apps.", "Topic :: Software Development :: Libraries :: Python Modules", ] [project.urls] Homepage = "https://github.com/mcs07/PubChemPy" Repository = "https://github.com/mcs07/PubChemPy" Documentation = "https://docs.pubchempy.org" Releases = "https://github.com/mcs07/PubChemPy/releases" "Issue Tracker" = "https://github.com/mcs07/PubChemPy/issues" [project.optional-dependencies] pandas = ["pandas>=0.16.2"] ssl = ["certifi>=2025.7.14"] [dependency-groups] dev = [ { include-group = "lint" }, { include-group = "docs" }, { include-group = "test" }, "pre-commit>=4.2.0", "ipykernel>=6.30.0", ] lint = ["ruff>=0.12.5", "nb-clean>=4.0.1"] docs = ["furo>=2025.7.19", "myst-parser>=3.0.1", "sphinx>=7.4.7"] test = ["pytest>=8.4.1", "pytest-rerunfailures>=15.1"] [tool.setuptools] py-modules = ["pubchempy"] [build-system] requires = ["setuptools>=77.0.3"] build-backend = "setuptools.build_meta" [tool.pytest.ini_options] addopts = "-v --reruns 3 --reruns-delay 5 --only-rerun TimeoutError --only-rerun ServerBusyError --only-rerun RemoteDisconnected --only-rerun URLError" testpaths = ["tests"] log_cli = true [tool.ruff] line-length = 88 target-version = "py39" [tool.ruff.lint] select = ["B", "D", "E", "F", "I", "W", "UP"] ignore = ["D213", "D203"] [tool.ruff.lint.per-file-ignores] "tests/*" = ["D"] [tool.ruff.lint.pydocstyle] convention = "google" [tool.ruff.lint.isort] combine-as-imports = true known-first-party = ["pubchempy"] mcs07-PubChemPy-1fcc2e5/scripts/000077500000000000000000000000001505763656600165015ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/scripts/make-docs000077500000000000000000000004621505763656600202740ustar00rootroot00000000000000#!/bin/bash # # Build documentation for PubChemPy using Sphinx. # Usage: uv run scripts/make-docs [html|clean|pdf|epub|dirhtml|...] set -e DOCS_DIR="$(dirname $(dirname "$0"))/docs" SPHINXOPTS="${SPHINXOPTS:-}" TARGET="${1:-html}" exec sphinx-build -M $TARGET "$DOCS_DIR" "$DOCS_DIR/_build" $SPHINXOPTS mcs07-PubChemPy-1fcc2e5/tests/000077500000000000000000000000001505763656600161545ustar00rootroot00000000000000mcs07-PubChemPy-1fcc2e5/tests/conftest.py000066400000000000000000000007411505763656600203550ustar00rootroot00000000000000"""Pytest configuration for PubChemPy tests.""" # import time # import pytest # @pytest.fixture(autouse=True) # def rate_limit_tests(): # """Limit the rate of requests to PubChem. # # This fixture ensures that there is a delay between tests to avoid hitting # PubChem's rate limits, which can cause PubChemHTTPError: 'PUGREST.ServerBusy' to be # raised. A delay of 1 second is automatically applied between every test. # """ # yield # time.sleep(1) mcs07-PubChemPy-1fcc2e5/tests/test_assay.py000066400000000000000000000014321505763656600207050ustar00rootroot00000000000000"""Test assay object.""" import pytest from pubchempy import Assay, ProjectCategory @pytest.fixture(scope="module") def a1(): """Assay AID 1973374.""" return Assay.from_aid(1973374) def test_basic(a1): assert a1.aid == 1973374 assert repr(a1) == "Assay(1973374)" assert a1.record def test_meta(a1): assert isinstance(a1.name, str) assert a1.project_category == ProjectCategory.LITERATURE_EXTRACTED assert isinstance(a1.description, list) assert isinstance(a1.comments, list) def test_assay_equality(): first = Assay.from_aid(1973374) second = Assay.from_aid(273821) assert first == first assert second == second assert first != second def test_assay_dict(a1): assert isinstance(a1.to_dict(), dict) assert a1.to_dict() mcs07-PubChemPy-1fcc2e5/tests/test_compound.py000066400000000000000000000124641505763656600214200ustar00rootroot00000000000000"""Test compound object.""" import re import warnings import pytest from pubchempy import ( Atom, BondType, Compound, PubChemPyDeprecationWarning, get_compounds, ) @pytest.fixture(scope="module") def c1(): """Compound CID 241.""" return Compound.from_cid(241) @pytest.fixture(scope="module") def c2(): """Compound CID 175.""" return Compound.from_cid(175) def test_basic(c1): """Test Compound is retrieved and has a record and correct CID.""" assert c1.cid == 241 assert repr(c1) == "Compound(241)" assert c1.record def test_atoms(c1): assert len(c1.atoms) == 12 assert {a.element for a in c1.atoms} == {"C", "H"} assert set(c1.elements) == {"C", "H"} def test_atoms_deprecated(c1): with warnings.catch_warnings(record=True) as w: assert {a["element"] for a in c1.atoms} == {"C", "H"} assert len(w) == 1 assert w[0].category == PubChemPyDeprecationWarning expected_message = ( "__getitem__ is deprecated: Dictionary style access to Atom attributes is " "deprecated" ) assert str(w[0].message) == expected_message def test_single_atom(): """Test Compound when there is a single atom and no bonds.""" c = Compound.from_cid(259) assert c.atoms == [Atom(aid=1, number=35, x=2, y=0, charge=-1)] assert c.bonds == [] def test_bonds(c1): assert len(c1.bonds) == 12 assert {b.order for b in c1.bonds} == {BondType.SINGLE, BondType.DOUBLE} def test_bonds_deprecated(c1): with warnings.catch_warnings(record=True) as w: assert {b["order"] for b in c1.bonds} == {BondType.SINGLE, BondType.DOUBLE} assert len(w) == 1 assert w[0].category == PubChemPyDeprecationWarning expected_message = ( "__getitem__ is deprecated: Dictionary style access to Bond attributes is " "deprecated" ) assert str(w[0].message) == expected_message def test_charge(c1): assert c1.charge == 0 def test_coordinates(c1): for a in c1.atoms: assert isinstance(a.x, (float, int)) assert isinstance(a.y, (float, int)) assert a.z is None def test_coordinates_deprecated(c1): with warnings.catch_warnings(record=True) as w: assert isinstance(c1.atoms[0]["x"], (float, int)) assert isinstance(c1.atoms[0]["y"], (float, int)) assert "z" not in c1.atoms[0] assert len(w) == 3 assert w[0].category == PubChemPyDeprecationWarning expected_message = ( "__getitem__ is deprecated: Dictionary style access to Atom attributes is " "deprecated" ) assert str(w[0].message) == expected_message def test_identifiers(c1): assert len(c1.connectivity_smiles) > 10 assert len(c1.smiles) > 10 assert c1.inchi.startswith("InChI=") assert re.match(r"^[A-Z]{14}-[A-Z]{10}-[A-Z\d]$", c1.inchikey) # TODO: c1.molecular_formula def test_properties_types(c1): assert isinstance(c1.molecular_weight, float) assert isinstance(c1.iupac_name, str) assert isinstance(c1.xlogp, float) assert isinstance(c1.exact_mass, float) assert isinstance(c1.monoisotopic_mass, float) assert isinstance(c1.tpsa, (int, float)) assert isinstance(c1.complexity, float) assert isinstance(c1.h_bond_donor_count, int) assert isinstance(c1.h_bond_acceptor_count, int) assert isinstance(c1.rotatable_bond_count, int) assert isinstance(c1.heavy_atom_count, int) assert isinstance(c1.isotope_atom_count, int) assert isinstance(c1.atom_stereo_count, int) assert isinstance(c1.defined_atom_stereo_count, int) assert isinstance(c1.undefined_atom_stereo_count, int) assert isinstance(c1.bond_stereo_count, int) assert isinstance(c1.defined_bond_stereo_count, int) assert isinstance(c1.undefined_bond_stereo_count, int) assert isinstance(c1.covalent_unit_count, int) assert isinstance(c1.fingerprint, str) def test_coordinate_type(c1): assert c1.coordinate_type == "2d" def test_compound_equality(): assert Compound.from_cid(241) == Compound.from_cid(241) assert get_compounds("Benzene", "name")[0] == get_compounds("c1ccccc1", "smiles")[0] def test_synonyms(c1): assert len(c1.synonyms) > 5 assert "benzene" in c1.synonyms def test_related_records(c1): assert len(c1.sids) > 20 assert len(c1.aids) > 20 def test_compound_dict(c1): assert isinstance(c1.to_dict(), dict) assert c1.to_dict() assert "atoms" in c1.to_dict() assert "bonds" in c1.to_dict() assert "element" in c1.to_dict()["atoms"][0] def test_charged_compound(c2): assert len(c2.atoms) == 7 assert c2.atoms[0].charge == -1 def test_charged_compound_deprecated(c2): with warnings.catch_warnings(record=True) as w: assert c2.atoms[0]["charge"] == -1 assert len(w) == 1 assert w[0].category == PubChemPyDeprecationWarning expected_message = ( "__getitem__ is deprecated: Dictionary style access to Atom attributes is " "deprecated" ) assert str(w[0].message) == expected_message def test_fingerprint(c1): # CACTVS fingerprint is 881 bits assert len(c1.cactvs_fingerprint) == 881 # Raw fingerprint has 4 byte prefix, 7 bit suffix, and is hex encoded (/4) = 230 assert len(c1.fingerprint) == (881 + (4 * 8) + 7) / 4 mcs07-PubChemPy-1fcc2e5/tests/test_compound3d.py000066400000000000000000000044661505763656600216520ustar00rootroot00000000000000"""Test compound object with 3D record.""" import warnings import pytest from pubchempy import Compound, PubChemPyDeprecationWarning @pytest.fixture def c3d(): """Compound CID 1234, 3D.""" return Compound.from_cid(1234, record_type="3d") def test_properties_types(c3d): assert isinstance(c3d.volume_3d, float) assert isinstance(c3d.multipoles_3d, list) assert isinstance(c3d.conformer_rmsd_3d, float) assert isinstance(c3d.effective_rotor_count_3d, int) assert isinstance(c3d.pharmacophore_features_3d, list) assert isinstance(c3d.mmff94_partial_charges_3d, list) assert isinstance(c3d.mmff94_energy_3d, float) assert isinstance(c3d.conformer_id_3d, str) assert isinstance(c3d.shape_selfoverlap_3d, float) assert isinstance(c3d.feature_selfoverlap_3d, float) assert isinstance(c3d.shape_fingerprint_3d, list) assert isinstance(c3d.volume_3d, float) def test_coordinate_type(c3d): assert c3d.coordinate_type == "3d" def test_atoms(c3d): assert len(c3d.atoms) == 75 assert {a.element for a in c3d.atoms} == {"C", "H", "O", "N"} assert set(c3d.elements) == {"C", "H", "O", "N"} def test_atoms_deprecated(c3d): with warnings.catch_warnings(record=True) as w: assert {a["element"] for a in c3d.atoms} == {"C", "H", "O", "N"} assert len(w) == 1 assert w[0].category == PubChemPyDeprecationWarning expected_message = ( "__getitem__ is deprecated: Dictionary style access to Atom attributes is " "deprecated" ) assert str(w[0].message) == expected_message def test_coordinates(c3d): for a in c3d.atoms: assert isinstance(a.x, (float, int)) assert isinstance(a.y, (float, int)) assert isinstance(a.z, (float, int)) def test_coordinates_deprecated(c3d): with warnings.catch_warnings(record=True) as w: assert isinstance(c3d.atoms[0]["x"], (float, int)) assert isinstance(c3d.atoms[0]["y"], (float, int)) assert isinstance(c3d.atoms[0]["z"], (float, int)) assert len(w) == 3 assert w[0].category == PubChemPyDeprecationWarning expected_message = ( "__getitem__ is deprecated: Dictionary style access to Atom attributes is " "deprecated" ) assert str(w[0].message) == expected_message mcs07-PubChemPy-1fcc2e5/tests/test_download.py000066400000000000000000000013701505763656600213750ustar00rootroot00000000000000"""Test downloading.""" import csv import pytest from pubchempy import download def test_image_download(tmp_path): download("PNG", tmp_path / "aspirin.png", "Aspirin", "name") with pytest.raises(OSError): download("PNG", tmp_path / "aspirin.png", "Aspirin", "name") download("PNG", tmp_path / "aspirin.png", "Aspirin", "name", overwrite=True) def test_csv_download(tmp_path): props = "property/ConnectivitySMILES,SMILES" download("CSV", tmp_path / "s.csv", [1, 2, 3], operation=props) with open(tmp_path / "s.csv") as f: rows = list(csv.reader(f)) assert rows[0] == ["CID", "ConnectivitySMILES", "SMILES"] assert rows[1][0] == "1" assert rows[2][0] == "2" assert rows[3][0] == "3" mcs07-PubChemPy-1fcc2e5/tests/test_errors.py000066400000000000000000000016451505763656600211070ustar00rootroot00000000000000"""Test errors.""" import pytest from pubchempy import ( BadRequestError, Compound, NotFoundError, Substance, get_compounds, get_substances, ) def test_invalid_identifier(): """BadRequestError should be raised if identifier is not a positive integer.""" with pytest.raises(BadRequestError): Compound.from_cid("aergaerhg") with pytest.raises(BadRequestError): get_compounds("srthrthsr") with pytest.raises(BadRequestError): get_substances("grgrqjksa") def test_notfound_identifier(): """NotFoundError should be raised if the record doesn't exist.""" with pytest.raises(NotFoundError): Compound.from_cid(999999999) with pytest.raises(NotFoundError): Substance.from_sid(999999999) def test_notfound_search(): """No error should be raised if a search returns no results.""" get_compounds(999999999) get_substances(999999999) mcs07-PubChemPy-1fcc2e5/tests/test_identifiers.py000066400000000000000000000021611505763656600220720ustar00rootroot00000000000000"""Test identifiers requests.""" from pubchempy import get_aids, get_cids, get_sids def test_identifiers_from_name(): """Use a name input to retrieve lists of identifiers.""" # Get CID for each compound linked to substances with name Aspirin assert len(get_cids("Aspirin", "name", "substance")) >= 10 # Get CID for each compound with name Aspirin assert len(get_cids("Aspirin", "name", "compound")) >= 1 # Get SID for substances linked to compound with name Aspirin assert len(get_sids("Aspirin", "name", "substance")) >= 10 # Get AID for each assay linked to substances with name Aspirin assert len(get_aids("Aspirin", "name", "substance")) >= 10 # Get AID for each assay linked to compound with name Aspirin assert len(get_aids("Aspirin", "name", "compound")) >= 1 def test_no_identifiers(): """Test retrieving no identifier results.""" assert get_cids("asfgaerghaeirughae", "name", "substance") == [] assert get_cids("asfgaerghaeirughae", "name", "compound") == [] assert get_sids(999999999, "cid", "compound") == [] assert get_aids(12568, "cid", "compound") == [] mcs07-PubChemPy-1fcc2e5/tests/test_pandas.py000066400000000000000000000033241505763656600210350ustar00rootroot00000000000000"""Test optional pandas functionality.""" import logging import pandas as pd from pubchempy import ( Compound, Substance, compounds_to_frame, get_compounds, get_properties, get_substances, substances_to_frame, ) log = logging.getLogger(__name__) def test_compounds_dataframe(): """""" df = get_compounds("C20H41Br", "formula", as_dataframe=True) assert df.ndim == 2 assert df.index.names == ["cid"] assert len(df.index) > 5 columns = df.columns.values.tolist() assert "atom_stereo_count" in columns assert "atoms" in columns assert "connectivity_smiles" in columns assert "exact_mass" in columns def test_substances_dataframe(): df = get_substances([1, 2, 3, 4], as_dataframe=True) assert df.ndim == 2 assert df.index.names == ["sid"] assert len(df.index) == 4 assert set(df.columns) == { "source_id", "source_name", "standardized_cid", "synonyms", } def test_properties_dataframe(): df = get_properties( ["smiles", "xlogp", "inchikey"], "1,2,3,4", "cid", as_dataframe=True ) assert df.ndim == 2 assert df.index.names == ["CID"] assert len(df.index) == 4 assert set(df.columns) == {"SMILES", "InChIKey", "XLogP"} def test_compound_series(): s = Compound.from_cid(241).to_series() assert isinstance(s, pd.Series) def test_substance_series(): s = Substance.from_sid(1234).to_series() assert isinstance(s, pd.Series) def test_compound_to_frame(): s = compounds_to_frame(Compound.from_cid(241)) assert isinstance(s, pd.DataFrame) def test_substance_to_frame(): s = substances_to_frame(Substance.from_sid(1234)) assert isinstance(s, pd.DataFrame) mcs07-PubChemPy-1fcc2e5/tests/test_properties.py000066400000000000000000000030211505763656600217550ustar00rootroot00000000000000"""Test properties requests.""" from pubchempy import get_properties, get_synonyms def test_properties(): """""" results = get_properties( ["SMILES", "InChIKey"], "tris-(1,10-phenanthroline)ruthenium", "name" ) assert len(results) > 0 for result in results: assert "CID" in result assert "SMILES" in result assert "InChIKey" in result def test_underscore_properties(): """Properties can be specified as snake_case and CamelCase.""" results = get_properties( ["smiles", "molecular_weight"], "tris-(1,10-phenanthroline)ruthenium", "name" ) assert len(results) > 0 for result in results: assert "CID" in result assert "SMILES" in result assert "MolecularWeight" in result def test_comma_string_properties(): """Properties can be specified as a comma-separated string rather than a list.""" results = get_properties( "smiles,InChIKey,molecular_weight", "tris-(1,10-phenanthroline)ruthenium", "name", ) assert len(results) > 0 for result in results: assert "CID" in result assert "SMILES" in result assert "MolecularWeight" in result assert "InChIKey" in result def test_synonyms(): results = get_synonyms("C1=CC2=C(C3=C(C=CC=N3)C=C2)N=C1", "smiles") assert len(results) > 0 for result in results: assert "CID" in result assert "Synonym" in result assert isinstance(result["Synonym"], list) assert len(result["Synonym"]) > 0 mcs07-PubChemPy-1fcc2e5/tests/test_requests.py000066400000000000000000000034561505763656600214500ustar00rootroot00000000000000"""Test basic requests.""" from pubchempy import get_json, get_sids, request def test_requests(): """Test basic raw requests and ensure they don't return an error code.""" assert request("c1ccccc1", "smiles").getcode() == 200 assert request("DTP/NCI", "sourceid", "substance", "747285", "SDF").getcode() == 200 assert ( request("coumarin", "name", output="PNG", image_size="50x50").getcode() == 200 ) def test_content_type(): """Test content type header matches desired output format.""" assert request(241, output="JSON").headers["Content-Type"] == "application/json" assert request(241, output="XML").headers["Content-Type"] == "application/xml" assert request(241, output="SDF").headers["Content-Type"] == "chemical/x-mdl-sdfile" assert request(241, output="ASNT").headers["Content-Type"] == "text/plain" assert request(241, output="PNG").headers["Content-Type"] == "image/png" def test_listkey_requests(): """Test asynchronous listkey requests.""" r1 = get_json("CC", "smiles", operation="cids", searchtype="superstructure") assert "IdentifierList" in r1 and "CID" in r1["IdentifierList"] r2 = get_json("C10H21N", "formula", listkey_count=3) assert "PC_Compounds" in r2 and len(r2["PC_Compounds"]) == 3 def test_xref_request(): """Test requests with xref inputs.""" response = request( "EP0711162A1", "PatentID", "substance", operation="sids", searchtype="xref" ) assert response.code == 200 response2 = get_json( "EP0711162A1", "PatentID", "substance", operation="sids", searchtype="xref" ) assert "IdentifierList" in response2 assert "SID" in response2["IdentifierList"] sids = get_sids("EP0711162A1", "PatentID", "substance", searchtype="xref") assert all(isinstance(sid, int) for sid in sids) mcs07-PubChemPy-1fcc2e5/tests/test_search.py000066400000000000000000000011011505763656600210230ustar00rootroot00000000000000"""Test searching.""" from pubchempy import get_assays, get_compounds def test_search_assays(): assays = get_assays([1, 1000, 490]) for assay in assays: assert isinstance(assay.name, str) def test_substructure(): results = get_compounds( "C1=CC2=C(C3=C(C=CC=N3)C=C2)N=C1", "smiles", searchtype="substructure", listkey_count=3, ) assert len(results) == 3 for result in results: assert all(el in [a.element for a in result.atoms] for el in {"C", "N", "H"}) assert result.heavy_atom_count >= 14 mcs07-PubChemPy-1fcc2e5/tests/test_sources.py000066400000000000000000000012571505763656600212550ustar00rootroot00000000000000"""Test depositor sources.""" from pubchempy import get_all_sources def test_substance_sources(): """Retrieve a list of all Substance sources.""" substance_sources = get_all_sources() assert len(substance_sources) > 20 assert isinstance(substance_sources, list) assert "SureChEMBL" in substance_sources assert "DiscoveryGate" in substance_sources assert "ZINC" in substance_sources def test_assay_sources(): """Retrieve a list of all Assay sources.""" assay_sources = get_all_sources("assay") assert len(assay_sources) > 20 assert isinstance(assay_sources, list) assert "ChEMBL" in assay_sources assert "DTP/NCI" in assay_sources mcs07-PubChemPy-1fcc2e5/tests/test_substance.py000066400000000000000000000027671505763656600215700ustar00rootroot00000000000000"""Test substance object.""" import pytest from pubchempy import Substance, get_substances 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