Skip to main content

Molecular machine learning representations

Project description

chemreps

License: MIT Documentation Status codecov Build Status Gitter Chat Binder

chemreps is a Python package for the creation of molecular representations for the purpose of machine learning. The molecular representations included in this library are implemented/adapted from current literature. The aim of chemreps is to provide an easy to use library for making molecular representations that can be then used with machine learning packages such as Scikit-Learn and Tensorflow.

Current Implementations

  • Coulomb Matrix
  • Bag of Bonds
  • Bonds/Nonbonding, Angles, Torsions
  • Just Bonds

The citations for the literature from which the representations are implemented/adapted from can be found in the source code for each representation.

Representation requests

Requests for new representations to be added can be made by raising an issue and labeling it as a feature request. Before requesting a new representation, please check under the Representation project in the Projects tab to see if that representation is included in the current work or progress.

Install

The latest release version can be installed with:

pip install chemreps

The latest development version can be installed by:

git clone https://github.com/chemreps/chemreps
cd chemreps
pip install -e .

Dependencies

chemreps requires:

  • Python (>=3.6)
  • NumPy (>=1.12)
  • cclib (>=1.5)

Contributing

If you are interested in helping develop for this project, please check out Contributing to chemreps in the wiki for a guide on how to get started.

Testing

Tests can be run in the top-level directory with the command pytest -v --cov=chemreps tests/

For help

If you need any help using chemreps, feel free to post in our Gitter.

Disclaimers:

  • These are attempts at the recreation of molecular representations from literature and may not be implemented properly.
    • If we do not implement something properly, feel free to make an issue.
  • This is solely a representation library and will not perform machine learning.

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

chemreps-0.1.0.tar.gz (14.3 kB view details)

Uploaded Source

Built Distribution

chemreps-0.1.0-py3-none-any.whl (19.0 kB view details)

Uploaded Python 3

File details

Details for the file chemreps-0.1.0.tar.gz.

File metadata

  • Download URL: chemreps-0.1.0.tar.gz
  • Upload date:
  • Size: 14.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.3

File hashes

Hashes for chemreps-0.1.0.tar.gz
Algorithm Hash digest
SHA256 67d3d857c80a71e1a73ca65f450dadb83a1a3764d8c899b32db1e9983fc0af81
MD5 ae3ea1320bcb10566177b4ee92766bc9
BLAKE2b-256 6a8ac84cb050246b1f282f09b2d65b17e80c0c0fdc795b592a7a18601b91a5c2

See more details on using hashes here.

File details

Details for the file chemreps-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: chemreps-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 19.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.3

File hashes

Hashes for chemreps-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ccc0473365337efeba7a297ce7cb49dd436901e0e619b9f4aa0bff185e7df9ad
MD5 e856e79521a78dc07e58ce38d887ec97
BLAKE2b-256 8dba990f38e249ec3ddb86236d99a1a37b879b5734e90c0371123fd77b2e5ec5

See more details on using hashes here.

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