npfc is a chemoinformatics tool for classifying Natural Product (NP) fragment combinations into predefined categories and therefore identifying pseudo-NPs.
Pseudo-NPs are novel NP-inspired compound classes that combine the biological relevance of NPs with the efficient exploration of chemical space by fragment-based drug design.
The npfc tool is written in Python and based on several key packages:
- RDKit for handling chemistry
- pandas for managing data into DataFrames
- NetworkX for modelling graphs
- Snakemake for encapsuling scripts into reproducible workflows
The npfc tool can be installed using PyPi. In your Python environment, run:
>>> pip install npfc
Warning!!! There is currently an issue when pytables is installed via pip. It was removed from the dependency but is required for the npfc workflow (reference file used during deduplication). Instead, please install pytables via conda:
>>> conda create -n npfc_env python pytables >>> conda activate npfc_env >>> pip install npfc
The full documentation is available at: https://npfc.readthedocs.io/. It describes the API, as well as the different workflows implemented. Moreover, a few tutorials are being included to show how to use this library in some practical use.
Feedback from the community is warmly welcomed. It can be in the form of bug reports and feature requests submitted via github or code contribution via forking this repo and submitting pull requests.
npfc is licensed under the MIT license.