Skip to main content

A tool for describing Natural Product- (NP) fragments combinations and identifying pseudo-NPs.

Project description

npfc: Natural Product Fragment Combinations

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

Installation

The npfc tool can be installed using PyPi. In your Python environment, run:

>>> pip install npfc
>>> conda install pytables

Documentation

The full documentation is available at: https://github.com/jose-manuel/npfc. It describes the API, as well as the different workflows implemented.

Contribution

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.

License

npfc is licensed under the MIT license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

npfc-0.7.16.tar.gz (12.9 MB view details)

Uploaded Source

File details

Details for the file npfc-0.7.16.tar.gz.

File metadata

  • Download URL: npfc-0.7.16.tar.gz
  • Upload date:
  • Size: 12.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/0.0.0 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.9.5

File hashes

Hashes for npfc-0.7.16.tar.gz
Algorithm Hash digest
SHA256 fb0093a7510861dcf8c982475ca749d12c8c9736794e0f9e4886e15ec888f501
MD5 5d5fd1d40f322318b427f0705e043267
BLAKE2b-256 fea6bacd30d7a3fe8f9a10b383481e31601b6e725c69210e467586d362b8656b

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page