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Project description
MolTrack Server
A lightweight, flexible and extendable FastAPI-based server for managing chemical compounds, batches, and properties, with the RDKit cartridge-enabled Postgres for chemical intelligence. Ideal for labs, startups, and small- to medium-sized biotech companies.
See also user stories
Features
Work in progress:
- Compound Registration
- Unique Identifiers: Automatically assigns unique identifiers (e.g., registration numbers, UUIDs) to new compounds.
- Duplicate Detection: Prevents registration of duplicates.
- Structure Validation: Valence checking, standardization of stereochemistry, etc.
- Structure Standardization: Converts entered structures into a consistent format, handling tautomerization, salts, and stereo conventions.
- Metadata
- Custom Attributes: Supports capturing custom metadata (e.g., biological data, physicochemical properties, origin information) and ties it to the appropriate entity (compound, batch/lot).
- Attachment Management: Allows attaching documents (NMR spectra, mass spectrometry data, analytical certificates).
- Batches and Lots
- Batch Registration: Manages registration of multiple batches or lots for a single compound.
- Duplicate Detection: Prevents the registration of duplicates
- Purity and Inventory Tracking: Tracks batch-specific details such as purity, quantity, storage location, supplier, and expiration dates.
- Protocols and Assay Results
- Protocols: Define assay types used to measure batches.
- Assay Results: Register and query assay results.
- Search
- Structure-based Search: Supports exact, substructure, similarity, and Markush searches.
- Metadata Search: Enables querying by metadata fields such as IDs, names, properties, and batch information.
- Audit and Compliance
- Audit Trails: Records detailed logs of registration, editing, and deletion activities for compliance and traceability.
- Role-based Access Control: Implements security controls to ensure sensitive data is accessible only by authorized users.
- Integration and APIs
- API Access: Provides RESTful APIs to facilitate integration with other lab informatics systems (ELNs, LIMS, inventory management systems).
- User Interface
- Chemical Drawing Integration: Allows users to input structures directly using chemical drawing tools (e.g., MarvinJS, ChemDraw, Ketcher).
- Custom Reports: Generates reports on compound libraries, registration statistics, and inventory statuses.
- Visualization Tools: Includes dashboards and data visualization features for quick analysis and decision-making.
Automated setup
To simplify and speed up the installation and launch process, we provide two automated setup scripts:
setup.batfor Windowssetup.shfor macOS/Linux
Manual setup can be time-consuming and error-prone, requiring multiple steps such as building Docker images, running containers, configuring virtual environments, and starting the server. These scripts handle all of that automatically, so you can get your environment ready with a single command.
Both scripts accept an optional --run_server flag (if specified, the Uvicorn server is started).
Note: Docker must be installed and running on your machine before running these scripts.
On Windows
- Open CMD in the project directory.
- Run:
setup.bat # Run setup only, skip starting server
setup.bat --run_server # Run setup and start server
On macOS/Linux
- Open a terminal in the project directory.
- Make the script executable and run:
chmod +x setup.sh
./setup.sh # Run setup only, skip starting server
./setup.sh --run_server # Run setup and start server
Manual setup
1. Create and activate a virtual environment
Create a new virtual environment:
python3 -m venv .venv
Activate the environment:
-
Windows (CMD):
.venv\Scripts\activate
-
macOS/Linux:
source .venv/bin/activate
2. Install uv
Install uv package using pip:
pip install uv
For alternative installation options, see the official docs.
3. Initialize the project environment with uv
Set up the virtual environment and dependencies using uv commands:
uv venv
uv sync
uv venvcreates the virtual environment.uv syncinstalls all required dependencies.
4. Configure the database connection
Edit the database.py file and update the SQLALCHEMY_DATABASE_URL variable with your PostgreSQL connection string:
SQLALCHEMY_DATABASE_URL = "postgresql://user:password@host:port/database"
Make sure your database server is running and accessible.
5. Run the server
Start the FastAPI server with:
uv run --active uvicorn app.main:app --reload
You can now access the API at http://localhost:8000.
Setting up pytest in VS Code
To configure pytest in VS Code, follow these steps:
-
Install the Python extension
- Open the Extensions view (
Ctrl+Shift+Xon Windows/Linux orCmd+Shift+Xon macOS). - Search for Python and install the official extension by Microsoft.
- Open the Extensions view (
-
Click the Testing icon (beaker icon) in the Activity bar.
-
Configure python tests
-
Click on Configure Python Tests button.
-
When prompted, select:
- Test framework:
pytest - Test directory: folder containing the tests (important: ensure it contains an
__init__.pyfile — this is required for test discovery to work properly)
- Test framework:
-
Your tests should now be detected and listed in the Testing panel.
API Documentation
Once the server is running, you can access:
- Interactive API documentation: http://localhost:8000/docs
- Alternative API documentation: http://localhost:8000/redoc
API Endpoints
Compounds
GET /compounds/- List all compoundsPOST /compounds/- Create a new compoundGET /compounds/{compound_id}- Get a specific compoundPUT /compounds/{compound_id}- Update a compoundDELETE /compounds/{compound_id}- Delete a compound
Batches
GET /batches/- List all batchesPOST /batches/- Create a new batchGET /batches/{batch_id}- Get a specific batchPUT /batches/{batch_id}- Update a batchDELETE /batches/{batch_id}- Delete a batch
Properties
POST /properties/- Create a new propertyGET /compounds/{compound_id}/properties/- Get properties for a compound
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