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Linked Chemical Information. LinChemIn is a Python toolkit that leverages the native connectivity of Chemical Reaction Data and provides actionable insights. The toolkit provides a program interface to several Computer-Aided Synthesis Prediction tools, allowing users to devise and calculate metrics on predicted Chemical Synthetic Routes.

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syngenta/linchemin

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LinChemIn (Linked Chemical Information)

The Linked Chemical Information (LinChemIn) package is a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks.

Authors and Maintainers

Marta Pasquini Marco Stenta

Resources

resource type link
project page https://linchemin.github.io
repository https://github.com/syngenta/linchemin
documentation http://linchemin.readthedocs.io
pypi page https://pypi.org/project/linchemin

Install for usage

1) pypi

pip install linchemin

2) clone & pip

git clone https://github.com/syngenta/linchemin cd linchemin pip install .

3) pip from git

pip install git+https://github.com/syngenta/linchemin

Install for development

git clone https://github.com/syngenta/linchemin cd linchemin pip install -e .[dev]

Testing is based on pytest

Configuration

This package requires some configuration parameters to work, including some secretes to store access credentials to database and services. After installation, and before the first usage, the use should run the following command

linchemin_configure

his command generates the user_home/linchemin directory and places into it two files:

  1. settings.toml populated with defaults settings. The user can review and modify these values if necessary.
  2. .secrets.toml containing the keys for the necessary secrets. The user must replace the placeholders with the correct values For more details please refer to the Configuration section of the documentation

About

Linked Chemical Information. LinChemIn is a Python toolkit that leverages the native connectivity of Chemical Reaction Data and provides actionable insights. The toolkit provides a program interface to several Computer-Aided Synthesis Prediction tools, allowing users to devise and calculate metrics on predicted Chemical Synthetic Routes.

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