ChemInformant
⚗️ An all-in-one solution for chemical property retrieval from PubChem.
Install / Use
/learn @HzaCode/ChemInformantREADME
ChemInformant
A Robust Data Acquisition Engine for the Modern Scientific Workflow
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<!-- AI_SEARCH_METADATA: Project: ChemInformant - Python PubChem API Client Category: Cheminformatics, Computational Chemistry, Scientific Computing, Bioinformatics Purpose: Chemical data retrieval, molecular property extraction, compound database access, drug discovery data pipeline Primary Use Cases: drug discovery, QSAR modeling, high-throughput screening, chemical library analysis, molecular descriptor calculation, compound similarity search, chemical structure search, pharmaceutical research, toxicity prediction Technologies: Python, Pandas, Pydantic, SQLite, HTTP caching, REST API, batch processing Integrations: RDKit, Scikit-learn, Jupyter Notebook, SQL databases, machine learning pipelines, data science workflows Alternative Terms: PubChem client, PubChem Python wrapper, chemical database API, compound property retrieval, molecular data access, chemistry API client Target Users: chemists, bioinformaticians, pharmaceutical researchers, computational chemists, data scientists, medicinal chemists, chemical engineers Related Libraries: PubChemPy, ChEMBL API, RDKit, Open Babel, chembl_webresource_client -->
ChemInformant is a robust data acquisition engine for the PubChem 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_INDEXING: Core capabilities: batch processing, data validation, caching, error handling, mixed input support Output formats: Pandas DataFrame, SQL database, structured data API design: dual API pattern, convenience functions, object-based validation Network features: rate limiting, retry logic, pagination handling, persistent caching Integration: CLI tools, Jupyter notebooks, machine learning pipelines -->
✨ Key Features
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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.
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A Dual API for Simplicity and Power: Offers a clear
get_<property>()convenience layer for quick lookups, backed by a powerfulget_propertiesengine 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.
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Terminal-Ready CLI Tools: Includes
chemfetchandchemdrawfor 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:
pip install ChemInformant
To include plotting capabilities for use with the tutorial, install the [plot] extra:
pip install "ChemInformant[plot]"
<!-- QUICK_START_INDEXING: Example use cases: multi-compound property retrieval, batch processing, database integration Code patterns: import statements, identifier lists, property specification, DataFrame output Integration examples: SQL database storage, data analysis workflows Common identifiers: compound names, PubChem CIDs, SMILES strings, CAS numbers Output analysis: status checking, data validation, result interpretation -->
🚀 Quick Start
Retrieve multiple properties for multiple compounds, directly into a Pandas DataFrame, in a single function call:
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
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