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Showing content from https://github.com/rdkit/rdkit below:

rdkit/rdkit: The official sources for the RDKit library

The RDKit is a collection of cheminformatics and machine-learning software written in C++ and Python.

• BSD license - a business friendly license for open source
• Core data structures and algorithms in C++
• Python 3.x wrapper generated using Boost.Python
• Java and C# wrappers generated with SWIG
• JavaScript (generated with emscripten) and CFFI wrappers around important functionality
• 2D and 3D molecular operations
• Descriptor and Fingerprint generation for machine learning
• Molecular database cartridge for PostgreSQL supporting substructure and similarity searches as well as many descriptor calculators
• Cheminformatics nodes for KNIME
• Contrib folder with useful community-contributed software harnessing the power of the RDKit

If you are working in Python and using conda (our recommendation), installation is super easy:

$ conda install -c conda-forge rdkit

You can then take a look at our Getting Started in Python guide.

More detailed installation instructions are available in Docs/Book/Install.md.

Available on the RDKit page and in the Docs folder on GitHub

The RDKit blog often has useful tips and tricks.

If you have questions, comments, or suggestions, the best places for those are:

• GitHub discussions
• The mailing list

If you've found a bug or would like to request a feature, please create an issue

We also have a LinkedIn group

We have a yearly user group meeting (the UGM) where members of the community do presentations and lightning talks on things they've done with the RDKit. Materials from past UGMs, which can quite useful, are also online:

• 2012 UGM, London
• 2013 UGM, Hinxton
• 2014 UGM, Darmstadt
• 2015 UGM, Zurich
• 2016 UGM, Basel
• 2017 UGM, Berlin
• 2018 UGM, Cambridge
• 2019 UGM, Hamburg
• 2020 UGM, virtual
• 2021 UGM, virtual
• 2022 UGM, Berlin
• 2023 UGM, Mainz
• 2024 UGM, Zurich

Code released under the BSD license.

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