Mongo-rdkit is an integration between MongoDB, a NoSQL database platform, and RDKit, a collection of cheminformatics and machine-learning software. This package contains tools to create and manipulate a chemically-intelligent database, as well as methods for high-performance searches on the database that leverage native MongoDB features.
Useful links:
- BSD License - a business friendly license for open-source.
- Jupyter Notebooks - walkthroughs for main functionality.
- Testing Guide - walkthrough of running
mongordkit
tests.
Jupyter Notebooks and resources for getting started in the docs folder on GitHub.
As the package is not officially configured with a setup.py file or pushed onto PyPi, these are working install instructions.
Ensure that you have either Anaconda or Miniconda installed and that conda
has been added to PATH
.
Clone the repository into your desired directory.
Navigate so that your current working directory is mongo-rdkit
.
Create a conda environment called mongo_rdkit that includes all dependencies needed for this package:
conda env create --quiet --force --file env.yml
Activate said conda environment:
source activate mongo_rdkit
Add the package mongo-rdkit to PYTHONPATH
:
export PYTHONPATH="$PWD"
Check that your/path/here/mongo-rdkit
lies in PYTHONPATH
:
echo $PYTHONPATH
You can now import mongordkit
in your Python interpreter or run all tests using the pytest
command.
Similarly, ensure that conda
has been added to PATH
.
Clone the repository into your desired directory and navigate into it.
Create a conda environment called mongo_rdkit that includes dependencies:
conda env create --quiet --force --file env.yml
Activate this conda environment:
call activate mongo_rdkit
Check that you are able to import mongordkit:
python -c "import mongordkit"
If this fails, you may need to add the current directory manually to PYTHONPATH
:
set PYTHONPATH=%PYTHONPATH%;C:.
You can now use mongordkit
in your interpreter and run tests using python -m pytest
.
mongordkit
contains two main modules, each of which contains a variety of importable methods and classes.
Database
contains functionality for writing and registering data. Search
contains functionality for setting up and performing
substructure and similarity search. Detailed walkthroughs can be found in the notebooks, listed below.
- Creating and Writing to MongoDB: documentation and demos for creating and modifying mongo-rdkit databases.
- Similarity and Substructure Search: documentation and demos for similarity and substructure search.
- Similarity Benchmarking: documentation for reproducing similarity benchmarking.
- Substructure Benchmarking: documentation for reproducing substructure benchmarking.
- azure_pipelines.yml: CI/CD pipeline configurations.
- conftest.py:
pytest
configurations. - env.yml: required dependencies.
Code released under the BSD License.