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RDKit

Azure build Status Documentation Status DOI

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
  • 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

Community

Code

Web presence

Materials from user group meetings

Documentation

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

Installation

Installation instructions are available in Docs/Book/Install.md.

Binary distributions, anaconda, homebrew

  • binaries for conda python or, if you are using the conda-forge stack, the RDKit is also available from conda-forge.
  • RPMs for RedHat Enterprise Linux, Centos, and Fedora. Contributed by Gianluca Sforna.
  • debs for Ubuntu and other Debian-derived Linux distros. Contributed by the Debichem team.
  • homebrew formula for building on the Mac. Contributed by Eddie Cao.
  • recipes for building using the excellent conda package manager. Contributed by Riccardo Vianello.
  • APKs for Alpine Linux. Contributed by da Verona
  • Wheels at PyPi for all major platforms and python versions. Contributed by Christopher Kuenneth

Projects using RDKit

  • chemprop - message passing neural networks for molecular property prediction
  • RMG - Reaction Mechanism Generator
  • RDMC - Reaction Data and Molecular Conformers - package for dealing with reactions, molecules, conformers, mainly in 3D
  • pychemprojections - python library for visualizing various 2D projections of molecules.
  • pychemovality - python library for estimating the ovality of molecules.
  • ChEMBL Structure Pipeline - ChEMBL protocols used to standardise and salt strip molecules.
  • FPSim2 - Simple package for fast molecular similarity searches.
  • Datamol (docs, repo) - A Python library to intuitively manipulate molecules.
  • Scopy (docs, paper) - an integrated negative design Python library for desirable HTS/VS database design
  • stk (docs, paper) - a Python library for building, manipulating, analyzing and automatic design of molecules.
  • gpusimilarity - A Cuda/Thrust implementation of fingerprint similarity searching
  • Samson Connect - Software for adaptive modeling and simulation of nanosystems
  • mol_frame - Chemical Structure Handling for Dask and Pandas DataFrames
  • RDKit.js - The official JavaScript release of RDKit
  • DeepChem - python library for deep learning for chemistry
  • mmpdb - Matched molecular pair database generation and analysis
  • CheTo (paper)- Chemical topic modeling
  • OCEAN (paper)- Optimized cross reactivity estimation
  • ChEMBL Beaker - standalone web server wrapper for RDKit and OSRA
  • ZINC - Free database of commercially-available compounds for virtual screening
  • sdf_viewer.py - an interactive SDF viewer
  • sdf2ppt - Reads an SDFile and displays molecules as image grid in powerpoint/openoffice presentation.
  • MolGears - A cheminformatics tool for bioactive molecules
  • PYPL - Simple cartridge that lets you call Python scripts from Oracle PL/SQL.
  • shape-it-rdkit - Gaussian molecular overlap code shape-it (from silicos it) ported to RDKit backend
  • WONKA - Tool for analysis and interrogation of protein-ligand crystal structures
  • OOMMPPAA - Tool for directed synthesis and data analysis based on protein-ligand crystal structures
  • OCEAN - web-tool for target-prediction of chemical structures which uses ChEMBL as datasource
  • chemfp - very fast fingerprint searching
  • rdkit_ipynb_tools - RDKit Tools for the IPython Notebook
  • Vernalis KNIME nodes
  • Erlwood KNIME nodes
  • AZOrange

License

Code released under the BSD license.

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  • HTML 65.0%
  • C++ 22.8%
  • Python 6.3%
  • C 2.5%
  • CMake 1.1%
  • Jupyter Notebook 0.8%
  • Other 1.5%