An overview of the RDKit

What is it?

Open source toolkit for cheminformatics

  • Business-friendly BSD license
  • Core data structures and algorithms in C++
  • Python (2.x and 3.x) wrapper generated using Boost.Python
  • Java and C# wrappers generated with SWIG
  • 2D and 3D molecular operations
  • Descriptor generation for machine learning
  • Molecular database cartridge for PostgreSQL
  • Cheminformatics nodes for KNIME (distributed from the KNIME community site:



  • 2000-2006: Developed and used at Rational Discovery for building predictive models for ADME, Tox, biological activity
  • June 2006: Open-source (BSD license) release of software, Rational Discovery shuts down
  • to present: Open-source development continues, use within Novartis, contributions from Novartis back to open-source version

Functionality overview


  • Input/Output: SMILES/SMARTS, SDF, TDT, SLN 1, Corina mol2 1, PDB, sequence notation, FASTA (peptides only), HELM (peptides only)
  • Substructure searching
  • Canonical SMILES
  • Chirality support (i.e. R/S or E/Z labeling)
  • Chemical transformations (e.g. remove matching substructures)
  • Chemical reactions
  • Molecular serialization (e.g. mol \<-> text)
  • 2D depiction, including constrained depiction
  • Fingerprinting: Daylight-like, atom pairs, topological torsions, Morgan algorithm, “MACCS keys”, extended reduced graphs, etc.
  • Similarity/diversity picking
  • Gasteiger-Marsili charges
  • Bemis and Murcko scaffold determination
  • Salt stripping
  • Functional-group filters


  • 2D pharmacophores 1
  • Hierarchical subgraph/fragment analysis
  • RECAP and BRICS implementations
  • Multi-molecule maximum common substructure 2
  • Enumeration of molecular resonance structures
  • Molecular descriptor library:
  • Topological (κ3, Balaban J, etc.)
  • Compositional (Number of Rings, Number of Aromatic Heterocycles, etc.)
  • Electrotopological state (Estate)
  • clogP, MR (Wildman and Crippen approach)
  • “MOE like” VSA descriptors
  • MQN 6
  • Similarity Maps 7
  • Machine Learning:
  • Clustering (hierarchical, Butina)
  • Information theory (Shannon entropy, information gain, etc.)
  • Tight integration with the Jupyter notebook (formerly the IPython notebook) and Pandas.


  • 2D->3D conversion/conformational analysis via distance geometry, including optional use of experimental torsion angle potentials.
  • UFF and MMFF94/MMFF94S implementations for cleaning up structures
  • Pharmacophore embedding (generate a pose of a molecule that matches a 3D pharmacophore) 1
  • Feature maps
  • Shape-based similarity
  • RMSD-based molecule-molecule alignment
  • Shape-based alignment (subshape alignment 3) 1
  • Unsupervised molecule-molecule alignment using the Open3DAlign algorithm 4
  • Integration with PyMOL for 3D visualization
  • Molecular descriptor library:
  • Feature-map vectors 5
  • Torsion Fingerprint Differences for comparing conformations 8

Integration with other open-source projects

  • KNIME: Workflow and analytics tool
  • Django: “The web framework for perfectionists with deadlines”
  • PostgreSQL: Extensible relational database
  • Lucene: Text-search engine 1

The Contrib Directory

The Contrib directory, part of the standard RDKit distribution, includes code that has been contributed by members of the community.

LEF: Local Environment Fingerprints

Contains python source code from the publications:

  • A. Vulpetti, U. Hommel, G. Landrum, R. Lewis and C. Dalvit, “Design and NMR-based screening of LEF, a library of chemical fragments with different Local Environment of Fluorine” J. Am. Chem. Soc. 131 (2009) 12949-12959.
  • Vulpetti, G. Landrum, S. Ruedisser, P. Erbel and C. Dalvit, “19F NMR Chemical Shift Prediction with Fluorine Fingerprint Descriptor” J. of Fluorine Chemistry 131 (2010) 570-577.

Contribution from Anna Vulpetti


Contains a set of pharmacophoric feature definitions as well as code for finding molecular frameworks.

Contribution from Markus Kossner

PBF: Plane of best fit

Contains C++ source code and sample data from the publication:

Firth, N. Brown, and J. Blagg, “Plane of Best Fit: A Novel Method to Characterize the Three-Dimensionality of Molecules” Journal of Chemical Information and Modeling 52 2516-2525 (2012).

Contribution from Nicholas Firth

mmpa: Matched molecular pairs

Python source and sample data for an implementation of the matched-molecular pair algorithm described in the publication:

Hussain, J., & Rea, C. “Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets.” Journal of chemical information and modeling 50 339-348 (2010).

Includes a fragment indexing algorithm from the publication:

Wagener, M., & Lommerse, J. P. “The quest for bioisosteric replacements.” Journal of chemical information and modeling 46 677-685 (2006).

Contribution from Jameed Hussain.

SA_Score: Synthetic assessibility score

Python source for an implementation of the SA score algorithm described in the publication:

Ertl, P. and Schuffenhauer A. “Estimation of Synthetic Accessibility Score of Drug-like Molecules based on Molecular Complexity and Fragment Contributions” Journal of Cheminformatics 1:8 (2009)

Contribution from Peter Ertl

fraggle: A fragment-based molecular similarity algorithm

Python source for an implementation of the fraggle similarity algorithm developed at GSK and described in this RDKit UGM presentation:

Contribution from Jameed Hussain

pzc: Tools for building and validating classifiers

Contribution from Paul Czodrowski

ConformerParser: parser for Amber trajectory files

Contribution from Sereina Riniker

NP_Score: Natural-product likeness score

Python source for an implementation of the NP score algorithm described in the publication:

“Natural Product Likeness Score and Its Application for Prioritization of Compound Libraries”
Peter Ertl, Silvio Roggo, and Ansgar Schuffenhauer
Journal of Chemical Information and Modeling 48:68-74 (2008)

Contribution from Peter Ertl


1: These implementations are functional but are not necessarily the best, fastest, or most complete.

2: Originally contributed by Andrew Dalke

3: Putta, S., Eksterowicz, J., Lemmen, C. & Stanton, R. “A Novel Subshape Molecular Descriptor” Journal of Chemical Information and Computer Sciences 43:1623–35 (2003).

4: Tosco, P., Balle, T. & Shiri, F. “Open3DALIGN: an open-source software aimed at unsupervised ligand alignment.” J Comput Aided Mol Des 25:777–83 (2011).

5: Landrum, G., Penzotti, J. & Putta, S. “Feature-map vectors: a new class of informative descriptors for computational drug discovery” Journal of Computer-Aided Molecular Design 20:751–62 (2006).

6: Nguyen, K. T., Blum, L. C., van Deursen, R. & Reymond, J.-L. “Classification of Organic Molecules by Molecular Quantum Numbers.” ChemMedChem 4:1803–5 (2009).

7: Riniker, S. & Landrum, G. A. “Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods.” Journal of Cheminformatics 5:43 (2013).

8: Schulz-Gasch, T., Schärfer, C., Guba, W. & Rarey, M. “TFD: Torsion Fingerprints As a New Measure To Compare Small Molecule Conformations.” J. Chem. Inf. Model. 52:1499–1512 (2012).


This document is copyright (C) 2013-2015 by Greg Landrum

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The intent of this license is similar to that of the RDKit itself. In simple words: “Do whatever you want with it, but please give us some credit.”