# Component Contribution
[](https://badge.fury.io/py/component-contribution)
[](https://anaconda.org/conda-forge/component-contribution)
[](https://www.python.org/downloads)
[](https://mit-license.org/)
[](https://gitlab.com/elad.noor/component-contribution/commits/develop)
[](https://codecov.io/gl/equilibrator/component-contribution)
[](https://groups.google.com/g/equilibrator-users)
[](https://equilibrator.readthedocs.io/en/latest/?badge=latest)
A method for estimating the standard reaction Gibbs energy of biochemical reactions.
## Cite us
For more information on the method behind component-contribution, please view our open
access paper:
Noor E, Haraldsdóttir HS, Milo R, Fleming RMT (2013)
[Consistent Estimation of Gibbs Energy Using Component Contributions](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003098),
PLoS Comput Biol 9:e1003098, DOI: 10.1371/journal.pcbi.1003098
Please, cite this paper if you publish work that uses `component-contribution`.
## Installation
* `pip install component-contribution`
## Dependencies
* Python 3.9+
* PyPI dependencies for prediction:
- equilibrator-cache
- numpy
- scipy
- pandas
- pint
- path
- periodictable
- uncertainties
* PyPI dependencies for training a new model:
- openbabel
- equilibrator-assets
## Data sources
* [Training data for the component contribution method](https://zenodo.org/record/3978440)
* [Chemical group definitions for the component-contribution method](https://zenodo.org/record/4010930)
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"description": "# Component Contribution\n\n[](https://badge.fury.io/py/component-contribution)\n[](https://anaconda.org/conda-forge/component-contribution)\n[](https://www.python.org/downloads)\n[](https://mit-license.org/)\n\n[](https://gitlab.com/elad.noor/component-contribution/commits/develop)\n[](https://codecov.io/gl/equilibrator/component-contribution)\n[](https://groups.google.com/g/equilibrator-users)\n[](https://equilibrator.readthedocs.io/en/latest/?badge=latest)\n\nA method for estimating the standard reaction Gibbs energy of biochemical reactions. \n\n## Cite us\n\nFor more information on the method behind component-contribution, please view our open\naccess paper:\n\nNoor E, Haraldsd\u00f3ttir HS, Milo R, Fleming RMT (2013)\n[Consistent Estimation of Gibbs Energy Using Component Contributions](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003098),\nPLoS Comput Biol 9:e1003098, DOI: 10.1371/journal.pcbi.1003098\n\nPlease, cite this paper if you publish work that uses `component-contribution`.\n\n## Installation\n\n* `pip install component-contribution`\n\n## Dependencies\n\n* Python 3.9+\n* PyPI dependencies for prediction:\n - equilibrator-cache\n - numpy\n - scipy\n - pandas\n - pint\n - path\n - periodictable\n - uncertainties\n* PyPI dependencies for training a new model:\n - openbabel\n - equilibrator-assets\n\n## Data sources\n\n* [Training data for the component contribution method](https://zenodo.org/record/3978440)\n* [Chemical group definitions for the component-contribution method](https://zenodo.org/record/4010930)\n",
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