| Name | ChemInformant JSON |
| Version |
2.4.0
JSON |
| download |
| home_page | None |
| Summary | A robust and high-throughput Python client for the PubChem API, designed for automated data retrieval and analysis |
| upload_time | 2025-07-30 23:51:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License
Copyright (c) 2024‑2025 Zhiang “Ang” He (HzaCode) <ang@hezhiang.com>
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.
|
| keywords |
chemistry
cheminformatics
pubchem
api
compound
drug
cache
pydantic
batch
smiles
sql
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# ChemInformant <img src="https://raw.githubusercontent.com/HzaCode/ChemInformant/main/images/logo.png" align="right" width="120px" />
[](https://pepy.tech/project/cheminformant)
[](https://app.codacy.com/gh/HzaCode/ChemInformant/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
<a href="https://joss.theoj.org/papers/b263ab8f865610c7c7a7f981035f78f7"><img src="https://joss.theoj.org/papers/b263ab8f865610c7c7a7f981035f78f7/status.svg"></a>
[](https://pypi.org/project/ChemInformant/)
[](https://pypi.org/project/ChemInformant/)
[](https://github.com/HzaCode/ChemInformant/actions/workflows/tests.yml)
[](https://github.com/psf/black)
[](https://github.com/HzaCode/ChemInformant/blob/main/LICENSE.md)
[](https://hezhiang.com/ChemInformant)
[](https://doi.org/10.5281/zenodo.16482964)
**ChemInformant** is a robust data acquisition engine for the [PubChem](https://pubchem.ncbi.nlm.nih.gov/) database, engineered for the modern scientific workflow. It intelligently manages network requests, performs rigorous runtime data validation, and delivers analysis-ready results, providing a dependable foundation for any computational chemistry project in Python.
---
### Key Features
* **Analysis-Ready Pandas/SQL Output:** The core API (`get_properties`) returns either a clean Pandas DataFrame or a direct SQL output, eliminating data wrangling boilerplate and enabling immediate integration with both the Python data science ecosystem and modern database workflows.
* **Automated Network Reliability:** Ensures your workflows run flawlessly with built-in persistent caching, smart rate-limiting, and automatic retries. It also transparently handles API pagination (`ListKey`) for large-scale queries, delivering complete result sets without any manual intervention.
* **Flexible & Fault-Tolerant Input:** Natively accepts mixed lists of identifiers (names, CIDs, SMILES) and intelligently handles any invalid inputs by flagging them with a clear status in the output, ensuring a single bad entry never fails an entire batch operation.
* **A Dual API for Simplicity and Power:** Offers a clear `get_<property>()` convenience layer for quick lookups, backed by a powerful `get_properties` engine for high-performance batch operations.
* **Guaranteed Data Integrity:** Employs Pydantic v2 models for rigorous, runtime data validation when using the object-based API, preventing malformed or unexpected data from corrupting your analysis pipeline.
* **Terminal-Ready CLI Tools:** Includes `chemfetch` and `chemdraw` for rapid data retrieval and 2D structure visualization directly from your terminal, perfect for quick lookups without writing a script.
* **Modern and Actively Maintained:** Built on a contemporary tech stack for long-term consistency and compatibility, providing a reliable alternative to older or less frequently updated libraries.
---
### Installation
Install the library from PyPI:
```bash
pip install ChemInformant
```
To include plotting capabilities for use with the tutorial, install the `[plot]` extra:
```bash
pip install "ChemInformant[plot]"
```
---
### Quick Start
Retrieve multiple properties for multiple compounds, directly into a Pandas DataFrame, in a single function call:
```python
import ChemInformant as ci
# 1. Define your identifiers
identifiers = ["aspirin", "caffeine", 1983] # 1983 is paracetamol's CID
# 2. Specify the properties you need
properties = ["molecular_weight", "xlogp", "cas"]
# 3. Call the core function
df = ci.get_properties(identifiers, properties)
# 4. Save the results to an SQL database
ci.df_to_sql(df, "sqlite:///chem_data.db", "results", if_exists="replace")
# 5. Analyze your results!
print(df)
```
**Output:**
```
input_identifier cid status molecular_weight xlogp cas
0 aspirin 2244 OK 180.16 1.2 50-78-2
1 caffeine 2519 OK 194.19 -0.1 58-08-2
2 1983 1983 OK 151.16 0.5 103-90-2
```
<details>
<summary><b>➡️ Click to see Convenience API Cheatsheet</b></summary>
<br>
| Function | Description |
|----------------------------|-----------------------------------------------|
| `get_weight(id)` | Molecular weight *(float)* |
| `get_formula(id)` | Molecular formula *(str)* |
| `get_cas(id)` | CAS Registry Number *(str)* |
| `get_iupac_name(id)` | IUPAC name *(str)* |
| `get_canonical_smiles(id)` | Canonical SMILES with Canonical→Connectivity fallback *(str)* |
| `get_isomeric_smiles(id)` | Isomeric SMILES with Isomeric→SMILES fallback *(str)* |
| `get_xlogp(id)` | XLogP (calculated hydrophobicity) *(float)* |
| `get_synonyms(id)` | List of synonyms *(List[str])* |
| `get_compound(id)` | Full, validated **`Compound`** object (Pydantic v2 model) |
*Note: This table shows key convenience functions for demonstration. ChemInformant provides **22 convenience functions** in total, covering molecular descriptors, mass properties, stereochemistry, and more.*
*All functions accept a **CID, name, or SMILES** and return `None`/`[]` on failure.*
</details>
ChemInformant also includes handy command-line tools for quick lookups directly from your terminal:
* **`chemfetch`**: Fetches properties for one or more compounds.
```bash
chemfetch aspirin --props "cas,molecular_weight,iupac_name"
```
* **`chemdraw`**: Renders the 2D structure of a compound.
```bash
chemdraw aspirin
```
---
### Documentation & Examples
For a deep dive, please see our detailed guides:
* **➡️ Online Documentation:** The **[official documentation site](https://hezhiang.com/ChemInformant)** contains complete API references, guides, and usage examples. **This is the most comprehensive resource.**
* **➡️ Interactive User Manual:** Our [**Jupyter Notebook Tutorial**](examples/ChemInformant_User_Manual_v1.0.ipynb) provides a complete, end-to-end walkthrough. This is the best place to start for a hands-on experience.
* **➡️ Performance Benchmarks:** You can review and run our [**Benchmark Script**](./benchmark.py) to see the performance advantages of batching and caching.
---
### Why ChemInformant?
ChemInformant's core mission is to serve as a high-performance data backbone for the Python cheminformatics ecosystem. By delivering clean, validated, and analysis-ready Pandas DataFrames, it enables researchers to effortlessly pipe PubChem data into powerful toolkits like RDKit, Scikit-learn, or custom machine learning models, transforming multi-step data acquisition and wrangling tasks into single, elegant lines of code.
A detailed comparison with other existing tools is provided in our [JOSS paper](https://github.com/HzaCode/ChemInformant/blob/main/paper/paper.md).
### Contributing
Contributions are welcome! For guidelines on how to get started, please read our [contributing guide](https://github.com/HzaCode/ChemInformant/blob/main/CONTRIBUTING.md). You can [open an issue](https://github.com/HzaCode/ChemInformant/issues) to report bugs or suggest features, or [submit a pull request](https://github.com/HzaCode/ChemInformant/pulls) to contribute code.
### License
This project is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details.
### Citation
If you use **ChemInformant** in your research, please cite **the software**
> **Software**: He, Z. *ChemInformant* [Software]. GitHub, https://github.com/HzaCode/ChemInformant.
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"description": "\r\n# ChemInformant <img src=\"https://raw.githubusercontent.com/HzaCode/ChemInformant/main/images/logo.png\" align=\"right\" width=\"120px\" />\r\n\r\n\r\n\r\n[](https://pepy.tech/project/cheminformant)\r\n[](https://app.codacy.com/gh/HzaCode/ChemInformant/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)\r\n\r\n<a href=\"https://joss.theoj.org/papers/b263ab8f865610c7c7a7f981035f78f7\"><img src=\"https://joss.theoj.org/papers/b263ab8f865610c7c7a7f981035f78f7/status.svg\"></a>\r\n[](https://pypi.org/project/ChemInformant/)\r\n[](https://pypi.org/project/ChemInformant/)\r\n[](https://github.com/HzaCode/ChemInformant/actions/workflows/tests.yml) \r\n\r\n[](https://github.com/psf/black)\r\n[](https://github.com/HzaCode/ChemInformant/blob/main/LICENSE.md)\r\n[](https://hezhiang.com/ChemInformant)\r\n[](https://doi.org/10.5281/zenodo.16482964)\r\n\r\n\r\n**ChemInformant** is a robust data acquisition engine for the [PubChem](https://pubchem.ncbi.nlm.nih.gov/) database, engineered for the modern scientific workflow. It intelligently manages network requests, performs rigorous runtime data validation, and delivers analysis-ready results, providing a dependable foundation for any computational chemistry project in Python.\r\n\r\n---\r\n\r\n### Key Features\r\n\r\n* **Analysis-Ready Pandas/SQL Output:** The core API (`get_properties`) returns either a clean Pandas DataFrame or a direct SQL output, eliminating data wrangling boilerplate and enabling immediate integration with both the Python data science ecosystem and modern database workflows.\r\n\r\n* **Automated Network Reliability:** Ensures your workflows run flawlessly with built-in persistent caching, smart rate-limiting, and automatic retries. It also transparently handles API pagination (`ListKey`) for large-scale queries, delivering complete result sets without any manual intervention.\r\n\r\n* **Flexible & Fault-Tolerant Input:** Natively accepts mixed lists of identifiers (names, CIDs, SMILES) and intelligently handles any invalid inputs by flagging them with a clear status in the output, ensuring a single bad entry never fails an entire batch operation.\r\n\r\n* **A Dual API for Simplicity and Power:** Offers a clear `get_<property>()` convenience layer for quick lookups, backed by a powerful `get_properties` engine for high-performance batch operations.\r\n\r\n* **Guaranteed Data Integrity:** Employs Pydantic v2 models for rigorous, runtime data validation when using the object-based API, preventing malformed or unexpected data from corrupting your analysis pipeline.\r\n\r\n* **Terminal-Ready CLI Tools:** Includes `chemfetch` and `chemdraw` for rapid data retrieval and 2D structure visualization directly from your terminal, perfect for quick lookups without writing a script.\r\n\r\n* **Modern and Actively Maintained:** Built on a contemporary tech stack for long-term consistency and compatibility, providing a reliable alternative to older or less frequently updated libraries.\r\n \r\n---\r\n\r\n### Installation\r\n\r\nInstall the library from PyPI:\r\n\r\n```bash\r\npip install ChemInformant\r\n```\r\n\r\nTo include plotting capabilities for use with the tutorial, install the `[plot]` extra:\r\n```bash\r\npip install \"ChemInformant[plot]\"\r\n```\r\n\r\n---\r\n\r\n### Quick Start\r\n\r\nRetrieve multiple properties for multiple compounds, directly into a Pandas DataFrame, in a single function call:\r\n\r\n```python\r\nimport ChemInformant as ci\r\n\r\n# 1. Define your identifiers\r\nidentifiers = [\"aspirin\", \"caffeine\", 1983] # 1983 is paracetamol's CID\r\n\r\n# 2. Specify the properties you need\r\nproperties = [\"molecular_weight\", \"xlogp\", \"cas\"]\r\n\r\n# 3. Call the core function\r\ndf = ci.get_properties(identifiers, properties)\r\n\r\n# 4. Save the results to an SQL database\r\nci.df_to_sql(df, \"sqlite:///chem_data.db\", \"results\", if_exists=\"replace\")\r\n\r\n# 5. Analyze your results!\r\nprint(df)\r\n\r\n\r\n```\r\n\r\n**Output:**\r\n\r\n```\r\n input_identifier cid status molecular_weight xlogp cas\r\n0 aspirin 2244 OK 180.16 1.2 50-78-2\r\n1 caffeine 2519 OK 194.19 -0.1 58-08-2\r\n2 1983 1983 OK 151.16 0.5 103-90-2\r\n```\r\n\r\n<details>\r\n<summary><b>\u27a1\ufe0f Click to see Convenience API Cheatsheet</b></summary>\r\n<br>\r\n\r\n| Function | Description |\r\n|----------------------------|-----------------------------------------------|\r\n| `get_weight(id)` | Molecular weight *(float)* |\r\n| `get_formula(id)` | Molecular formula *(str)* |\r\n| `get_cas(id)` | CAS Registry Number *(str)* |\r\n| `get_iupac_name(id)` | IUPAC name *(str)* |\r\n| `get_canonical_smiles(id)` | Canonical SMILES with Canonical\u2192Connectivity fallback *(str)* |\r\n| `get_isomeric_smiles(id)` | Isomeric SMILES with Isomeric\u2192SMILES fallback *(str)* |\r\n| `get_xlogp(id)` | XLogP (calculated hydrophobicity) *(float)* |\r\n| `get_synonyms(id)` | List of synonyms *(List[str])* |\r\n| `get_compound(id)` | Full, validated **`Compound`** object (Pydantic v2 model) |\r\n\r\n*Note: This table shows key convenience functions for demonstration. ChemInformant provides **22 convenience functions** in total, covering molecular descriptors, mass properties, stereochemistry, and more.*\r\n\r\n*All functions accept a **CID, name, or SMILES** and return `None`/`[]` on failure.*\r\n\r\n</details>\r\n\r\n\r\n\r\nChemInformant also includes handy command-line tools for quick lookups directly from your terminal:\r\n\r\n* **`chemfetch`**: Fetches properties for one or more compounds.\r\n ```bash\r\n chemfetch aspirin --props \"cas,molecular_weight,iupac_name\"\r\n ```\r\n\r\n* **`chemdraw`**: Renders the 2D structure of a compound.\r\n ```bash\r\n chemdraw aspirin\r\n ```\r\n---\r\n### Documentation & Examples\r\n\r\nFor a deep dive, please see our detailed guides:\r\n\r\n* **\u27a1\ufe0f Online Documentation:** The **[official documentation site](https://hezhiang.com/ChemInformant)** contains complete API references, guides, and usage examples. **This is the most comprehensive resource.**\r\n* **\u27a1\ufe0f Interactive User Manual:** Our [**Jupyter Notebook Tutorial**](examples/ChemInformant_User_Manual_v1.0.ipynb) provides a complete, end-to-end walkthrough. This is the best place to start for a hands-on experience.\r\n* **\u27a1\ufe0f Performance Benchmarks:** You can review and run our [**Benchmark Script**](./benchmark.py) to see the performance advantages of batching and caching.\r\n\r\n---\r\n\r\n### Why ChemInformant?\r\n\r\nChemInformant's core mission is to serve as a high-performance data backbone for the Python cheminformatics ecosystem. By delivering clean, validated, and analysis-ready Pandas DataFrames, it enables researchers to effortlessly pipe PubChem data into powerful toolkits like RDKit, Scikit-learn, or custom machine learning models, transforming multi-step data acquisition and wrangling tasks into single, elegant lines of code.\r\n\r\nA detailed comparison with other existing tools is provided in our [JOSS paper](https://github.com/HzaCode/ChemInformant/blob/main/paper/paper.md).\r\n\r\n### Contributing\r\n\r\nContributions are welcome! For guidelines on how to get started, please read our [contributing guide](https://github.com/HzaCode/ChemInformant/blob/main/CONTRIBUTING.md). You can [open an issue](https://github.com/HzaCode/ChemInformant/issues) to report bugs or suggest features, or [submit a pull request](https://github.com/HzaCode/ChemInformant/pulls) to contribute code.\r\n### License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details.\r\n### Citation\r\n\r\nIf you use **ChemInformant** in your research, please cite **the software** \r\n> **Software**: He, Z. *ChemInformant* [Software]. GitHub, https://github.com/HzaCode/ChemInformant. \r\n",
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