Open Source Molecular Modeling

AbstractThe success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry.


What is Open Source?

Free and open source software (FOSS) is software that is both considered “free software,” as defined by the Free Software Foundation ( and “open source,” as defined by the Open Source Initiative ( The distinctions between free and open source software are largely philosophical - the free software movement is primary motivated by user freedoms (”free as in speech, not free as in beer”) while the open source movement is more concerned with promoting an open development model to enhance software quality. However, as a practical matter, especially with regards to scientific software, such distinctions remain philosophical rather than practical as the most popular software licenses are both free and open source.

The unifying theme of open source software licenses is that they allow users to use, modify, and distribute software without significant restrictions. This is achieved by making the full source code of the software available to users. Broadly speaking, open source licenses fall into two categories: permissive and copyleft. Permissive licenses, such as the Apache, BSD, MIT, and Python licenses, place minimal restrictions on how modified code may be distributed, such as requiring attribution and limiting liability. They specifically do not require that redistributions of modified source code be licensed under the same license as the original source code. This enables source code licensed under a permissive license to be incorporated into commercial, proprietary programs that are not open source. In contrast, copyleft licenses, such as the different versions of the GNU Public License (GPL), require that public redistributions of licensed software remain licensed under a GPL license. That is, the source code must remain publicly and freely available. The GNU Lesser General Public License (LGPL) is slightly less restrictive version of the GPL used primarily for libraries as it does not require software that uses LGPL licensed software as a library to be licensed under the LGPL. Although copyleft licenses do not prohibit selling software, since the full source code must remain freely available, in practice vendors of copylefted software must commercialize the support of the product, rather than the product itself. Finally, we note there are other software licenses that make source code available, but are not open source licenses. These licenses typically prohibit the redistribution of the source code. Such licenses, which we will refer to as “source-available” licenses, have some popularity in academia as they allow source code to be distributed to other researchers in non-profit institutions, but allow the code to be sold to commercial entities.

Advantages and Disadvantages of Open Source

The value of open source software in cheminformatics and molecular modeling is somewhat controversial. Unsurprisingly, those affiliated with commercial scientific software argue that traditional commercial development, with its associated support and continuous development, provides a superior value (Krylov 2015), while open source advocates feel the benefits outweigh the burdens (Gezelter 2015, Jacob 2016). Our goal is not to revisit these arguments. Instead, we assert that open source scientific software is a de facto part of the scientific community, and so in this review we catalog those open source packages that fall within the domain of cheminformatics and molecular modeling.

There are a few aspects of the open source software debate that we find particularly relevant. First, opponents are right to point out that free software is not free - users of open source software generally take on a much greater burden in supporting the software than with commercial software. This is one reason why it is important, when possible, to seek open source software that is under active development and supported by a broad community. Therefore, in this review we attempt to quantify the current level of development and usage of each package as an indirect measure of quality and usability. Second, the primary advantage of open source software is the ability to redistribute code without restriction. This inherently enables reproducibility and lets scientists “stand on the shoulders of giants” instead of reinventing the wheel. Consequently, in this review we limit ourselves to a survey of true open source software and exclude source-available software that may place restrictions on the publication of reproducible research results.


We organize software packages into seven categories: cheminformatics, visualization, QSAR (quantitative structure–activity relationship) and ADMET modeling, quantum chemistry, ligand dynamics and free energy calculations, and virtual screening (including ligand design). We identified open source software packages by browsing the relevant categories (Molecular Science, Chemistry, Bio-Informatics, Medical Science) on the popular SourceForge ( repository, searching for categories on GitHub (, searching for categories on OpenHub (, searching for categories together with “open source software” on Google, and browsing the Click2Drug ( and VLS3D (Villoutreix 2013) directories. Finally, a draft document was tweeted (@david_koes) to solicit suggestions for additional packages from the community.

For every identified software package, we report its primary URL and software license and assign it an activity code. For simplicity, BSD-like licenses (e.g. NCSA) are reported as BSD. Activity codes consist of a development activity level (alphabetical) and usage activity level (numerical). Activity codes were assigned as follows:

Development Activity


Substantial development (e.g. a new major release, the addition of new features, or substantial refinements of existing features) within the last 18 months. Note this includes all projects that were created in the last 18 months.


Evidence of some development within the last 18 months such as a minor release or bug fixes to a development branch.


No evidence of development (changes to the source code or documentation) within the last 18 months. Note that in cases where a package does not follow an open development model (i.e., source is only released with official releases) the estimate of development activity will be overly conservative.

Usage Activity

  1. 1.

    Substantial user usage within the last 18 months (more than 20 downloads a month on average from SourceForge, more than 20 stars or forks on GitHub, more than 10 citations a year, and/or a clearly active user community as indicated by traffic on mailing lists or discussion boards).

  2. 2.

    Moderate user usage within the last 18 months.

  3. 3.

    Minimal or no identifiable user usage within the last 18 months (fewer than 50 downloads total on SourceForge, three or fewer stars and/or forks on GitHub, or fewer than one citation a year).

We omit some packages with extended periods of inactivity (e.g. more than 10 years) where there is little evidence of any usage or packages that are referenced in the literature but for which we could not find a extant source code repository. We also omit packages that provide common and/or trivial functionality (e.g. molecular weight calculators) and those that require non-open source packages in order to function.


Cheminformatics involves the representation, manipulation and analysis of molecular data (Leach 2007, Gasteiger 2003). Cheminformatic toolkits, although they may contain standalone utility programs, are primarily designed to function as libraries for other programs so that common functions, such as parsing molecular data, need not be reimplemented. As libraries, the native programming language of a toolkit is particularly relevant as it influences the language programs that integrate with the toolkit can be written in. In some cases, alternative language bindings, which essentially translate between programming languages, may be available, but due to the use of different idioms by different languages (e.g. object-oriented vs. functional, manual vs. automatic memory management) use of these non-native bindings may be cumbersome. In addition to toolkits, we catalog standalone programs, including conformer generators for converted 2D information into 3D molecular structures (some of which have been critically evaluated (Ebejer 2012)), and graphical environments for creating and managing workflows.

\label{chemtool} Open source cheminformatics toolkits.
Name URL License Activity Citation
BALL LGPL A2 (Hildebrandt 2010)
CDK LGPL A1 (Steinbeck 2006)
Chem\({}^{f}\) GPL C2 (Höck 2012)
chemfp MIT C3 (Dalke 2011)
chemkit BSD B1
ChemmineR Artistic A1 (Cao 2008)
Cinfony BSD/GPL B1 (OBoyle 2008)
CurlySMILES GPL C2 (Drefahl 2011)
DisCuS GPL B3 (Wójcikowski 2014)
Fafoom LGPL A2 (Supady 2015)
fmcsR Artistic A1 (Wang 2013)
frowns Python C2
Helium BSD B2
Indigo GPL A1 (Pavlov 2011)
JoeLib GPL C1
LICSS GPL A2 (Lawson 2012)
MayaChemTools LGPL A2 (Sud 2012)
Mychem GPL B2
ODDT BSD A2 (Wójcikowski 2015)
Open Babel GPL A1 (OBoyle 2011)
OPSIN Artistic A1 (Lowe 2011)
OrChem LGPL C2 (Rijnbeek 2009)
osra GPL A1 (Filippov 2009)
pybel GPL A1 (OBoyle 2008a)
rcdk LGPL B2 (Guha 2007)
RInChI Apache A3
rpubchem GPL C3
rubabel MIT C2 (Smith 2013)
SMSD CCAL B2 (Rahman 2009)
Som-itTM LGPL C3
webchem MIT A2
\label{chemgui} Open source graphical development environments for cheminformatics.
Name URL License Activity Citation
AMBIT GPL A1 (Jeliazkova 2011)
Bioclipse Eclipse B1 (Spjuth 2009)
Galaxy Tool Academic A1 (Goecks 2010)
KNIME GPL A1 (Berthold 2009)
Orange BSD A1 (Demšar 2013)
SA2 GPL A1 (Guilloux 2012)
Taverna LGPL A1 (Wolstencroft 2013)
Weka GPL A1 (Hall 2009)

Toolkits (Table \ref{chemtool})

The Biochemical Algorithms Library (BALL) (Hildebrandt 2010) provides an object-oriented C++ library for structural bioinformatics, and its capabilities include molecular mechanics, support for reading and writing a variety of file formats, protein-ligand scoring, docking, and QSAR modeling.

The Chemistry Development Kit (CDK) (Steinbeck 2006) is a cheminformatics toolkit written in Java. Its capabilities include support for reading and writing a variety of chemical formats, descriptor and fingerprint calculation, force field calculations, substructure search, and structure generation.

Chem\({}^{f}\) (Höck 2012) is a minimal cheminformatics toolkit written in the functional language Scala.

chemfp (Dalke 2011) is a high-performance library with a Python interface for generating and searching for molecular fingerprints.

chemkit is a C++ cheminformatics toolkit that includes support for visualization with the Qt framework and molecular modeling.

ChemmineR (Cao 2008) is a cheminformatics package for the R statistical programming language that is built using Open Babel. Its capabilities include property calculations, similarity search, and classification and clustering of compounds.

Cinfony (OBoyle 2008) provides a single, simple standardized interface to other cheminformatics toolkits, including Open Babel, RDKit, the CDK, Indigo, JChem, OPSIN, and several web services.

CurlySMILES (Drefahl 2011) provides parsing functionality for an extension of the SMILES format that supports the description of complex molecular systems.

DisCuS (Database System for Compound Selection) (Wójcikowski 2014) provides support for analyzing the results of a high-throughput screen.

Fafoom (flexible algorithm for optimization of molecules) (Supady 2015) is a Python library for identifying low energy conformers using a genetic algorithm.

fmcsR (Goecks 2010) is an R package that efficiently performs flexible maximum common substructure matching that allows minor mismatches between atoms and bonds in the common substructure.

Frowns is a cheminformatics toolkit mostly written in Python that provides basic support for SMILES and SD files, SMARTS search, fingerprint generation, and property perception.

Helium is a cheminformatics toolkit written using modern C++ idioms that provides support for SMILES files, fingerprints generation, and SMARTS and SMIRKS.

Indigo (Pavlov 2011) is a cheminformatics toolkit written in C++ with C, Python, Java (including a KNIME node), and C# bindings. Its capabilities include general support for manipulating molecules, property calculation, combinatorial chemistry, scaffold detection and R-group decomposition, reaction processing, substructure matching and similarity search.

JOELib is a cheminformatics toolkit written in Java. Its capabilities include SMARTS substructure search, descriptor calculation, and processing/filtering pipes.

LICSS (Lawson 2012) integrates with the CDK to provide representations and analysis of chemical data embedded within Microsoft Excel.

MayChemTools is a collection of Perl scripts for manipulating chemical data, interfacing with databases, generating fingerprints, performing similarity search, and computing molecular properties.

Mychem is built using OpenBabel and provides an extension to the MySQL database package that adds the ability to search, analyze, and convert chemical data within a MySQL database.

The Open Drug Discovery Toolkit (ODDT) (Wójcikowski 2015) is entirely written in Python, is built on top of RDKit and Open Babel, and is focused on providing enhanced functionality for managing and implementing drug discovery workflows, such as making it easy to implement a docking pipeline.

Open Babel (OBoyle 2011) is substantial cheminformatics toolkit written in C++ with Python, Perl, Java, Ruby, R, PHP, and Scala bindings. Its capabilities include support for more than 100 chemical file formats, fingerprint generation, property determination, similarity and substructure search, structure generation, and molecular force fields. It has absorbed the Confab (OBoyle 2011a) conformer generator which produces 3D structures through the systematic enumeration of torsions and energy minimization.

OPSIN (Lowe 2011), the Open Parser for Systematic IUPAC nomenclature, converts plain-text chemical nomenclature to machine readable CML or InChi formats.

OrChem is built using the CDK and provides an extension to Oracle databases that adds the ability to incorporate and search chemical data.

OSRA (Filippov 2009) provides optical structure recognition. It takes as input an image and generates a SMILES string.

Ouch (Ouch Uses Chemical Haskell) is a minimal cheminformatics toolkit written in the functional language Haskell.

Pybel (OBoyle 2008a) provides the full functionality of Open Babel, but common routines are provided in a simplified, more ‘pythonic’ interface.

rcdk (Guha 2007) provides an R interface to the CDK and working with fingerprints.

RDKit is a substantial cheminformatics toolkit written in C++ with Python, Java and C# bindings. Its capabilities include file handling, manipulation of molecular data, chemical reactions, substantial support for fingerprinting, substructure and similarity search, 3D conformer generation, property determination, force field support, shape-based alignment and screening, and integration with PyMOL, KNIME, and PostgreSQL.

RInChI provides tools for creating and manipulating reaction InChIs, a unique string for describing a reaction.

rpubchem is an R package for interfacing with the PubChem database.

rubabel (Smith 2013) is similar to Pybel in that it provides a native Ruby interface to Open Babel.

The Small Molecule Subgraph Detector (SMSD) (Rahman 2009) is a Java library for calculating the maximum common subgraph between small molecules.

Som-itTM is an R package for creating and visualizing self-organizing maps from large datasets.

webchem is an R package for interfacing with a dozen different on-line resources for chemical data.

Standalone Programs (Table \ref{standalone})

cApp (Amani 2015) is a Java application that provides tools for evaluating physico-chemical properties, performing similarity searches, and querying the PubChem database.

The utilities checkmol and matchmol (Haider 2010) decompose and compare functional groups of input molecules.

ConvertMAS is a utility for converting between formats and merging and splitting multi-molecule files.

Filter-itTM filters a set of molecules based on their properties such as physicochemical parameters and graph-based properties.

Frog2 (Miteva 2010) uses a two stage Monte Carlo approach coupled with energy minimization to rapidly generate 3D conformers.

The Lilly MedChem Rules (LMR) (Bruns 2012) apply filters to avoid reactive and promiscuous compounds.

Molpher (Hoksza 2014) generates a virtual chemical library that represents the chemical space between two input molecules as it consists of the path found by morphing one molecule to another.

MoSS (Molecular Subsstructure miner) (Borgelt 2005) finds common molecular substructures and discriminative fragments within a compound library.

The Open Molecule Generator (OMG) (Peironcely 2012) enumerates all possible chemical structures given constraints on their composition.

sdf2xyz2sdf (Tosco 2011) converts between SDF and TINKER XYZ files.

sdsorter provides convenient routines for manipulating, sorting, and filtering the contents of sdf molecular data files based on the embedded sd data tags.

Shape (Rosen 2009) employs a genetic algorithm to generate conformations of carbohydrates.

Strip-itTM is built using Open Babel and extracts molecular scaffolds.

Graphical Development Environments (Table \ref{chemgui})

Ambit (Jeliazkova 2011) integrates with the CDK to provide web-based applications for chemical search and analysis and includes a tautomer generation algorithm (Kochev 2013).

Bioclipse (Spjuth 2009) is a workbench, based on the Eclipse framework, for manipulating and analyzing biochemical data and databases. It integrates with the CDK and Jmol to provide cheminformatic functionality and also has modules for bioinformatics (primarly sequence analysis) and QSAR modeling.

Galaxy (Goecks 2010) is a web platform for exploring biomedical data and includes as a component a Chemical Toolbox that integrates a number of other cheminformatics tools to offer an array of molecular search, property calculation, clustering, and manipulation capabilities.

The Konstanz Information Miner (KNIME) is a general workflow environment that includes a number of plugins for cheminformatics, such as CDK (Beisken 2013) and RDKit modules, as well as bioinformatics and machine learning modules.

Orange (Demšar 2013) is a graphical interface for construction interactive workflows and performing data analysis and visualization.

Screening Assistant 2 (SA2) (Guilloux 2012) is a GUI written in Java that integrates with other toolkits to help manage, analyze, and visualize libraries of compounds.

Taverna (Wolstencroft 2013) is a graphical workflow editor that includes support for integrating with web services and the CDK (Kuhn 2010).

Weka (Hall 2009) is a platform for data mining and machine learning that can be adapted for cheminformatics.


An essential component of any molecular modeling exercise is the ability to visualize and, sometimes, edit molecular data. Visualization software usually either deals with exclusively 2D or 3D molecular data and may be primarily intended for desktop usage (native ‘fat’ clients) or as a component embedded in a web browser. Packages designed for visualizing entire datasets are cataloged in the QSAR section (Table \ref{qsarviz}) and those with a particular emphasize on visualizing the output of quantum chemistry packages are in the Quantum Chemistry section (Table \ref{qmviz}).

\label{2ddesktopviz} Open source tools for 2D molecular visualization and editing on the desktop.
Name URL License Activity Citation
BKchem GPL C3
chemfig LaTeX A2
Chemtool GPL B3
JChemPaint LGPL B1 (Krause 2000)
LeView GPL B3 (Caboche 2013)
mol2chemfig LaTeX C3 (Brefo-Mensah 2012)
Molsketch GPL A1
SketchEl GPL A1

2D Desktop Applications (Table \ref{2ddesktopviz})

BKChem is a 2D molecular editor written in python that uses the Tk GUI toolkit.

chemfig is a tool for embedding chemical drawings in LaTeX documents.

Chemtool is a 2D molecular editor for Linux systems that uses the GTK toolkit.

JChemPaint (Krause 2000) is a Java-based 2D molecular editor built using the CDK toolkit.

LeView (Caboche 2013) generates 2D representations of ligand-protein interactions that highlight features such as hydrogen bonds.

mol2chemfig (Brefo-Mensah 2012) converts SMILES files into LaTeX source code.

Molsketch is a 2D molecular editor written in C++ with the Qt toolkit that includes support for the Windows and Android operating systems.

SketchEl is a Java-based 2D molecular editor that includes support for a datasheet view for handling multi-molecule files.

3D Desktop Applications (Table \ref{3ddesktopviz})

Avogadro (Hanwell 2012) is a 3D molecular viewer and editor with a modular plugin architecture with both Python and C++ bindings that includes interactive structure optimization for real-time editing.

BALLView (Moll 2005) provides interactive 3D visualizations as part of the BALL (Hildebrandt 2010) cheminformatics toolkit.

gMol provides basic interactive 3D visualizations of molecular data readable by Open Babel.

Jamberoo provides a basic Java-based 3D molecular viewer and editor.

LP Molecular Viewer is an ActiveX/ATL control for embedding interactive 3D representations of molecular data in Microsoft products.

Luscus (Kovačević 2015) is a 3D viewer and editor that is designed with a focus on electronic structure information.

Molecular Rift (Norrby 2015) integrates with the Oculus Rift virtual reality headset to provide immersive visualization of 3D molecular data.

OpenStructure (Biasini 2013) is a computational structural biology framework that provides a 3D viewer for manipulating structural information and includes an interactive Python shell.

PLIP (Protein-Ligand Interaction Profiler) (Salentin 2015) runs as a web application and analyzes and visualizes protein-ligand interactions in 3D.

PyMOL is a substantial 3D molecular viewer that includes a full Python interface to support scripting and plugin development.

RasTop and OpenRasMol are based off the venerable RasMol software and provide basic 3D visualization.

SPADE (Structural Proteomics Application Development Environment) (Sweeney 2011) is a graphical Python interface for structural informatics.

QuteMol (Tarini 2006) provides high-quality, visually engaging renderings of 3D molecular data.

Web-Based Visualization (Table \ref{webviz})

3Dmol.js (Rego 2014) is a JavaScript library that provides WebGL-accelerated interactive 3D visualizations of molecular structures and surfaces.

CH5M3D (Earley 2013) uses JavaScript and HTML5 to provide visualization and editing of 3D structures of small molecules.

Chemozart (Mohebifar 2015) is a WebGL-based web application for 3D editing of small molecules.

CWC (ChemDoodle Web Components) (Burger 2015) provides a suite of web-based visualizers and editors for 2D and 3D molecular data.

JSME (Bienfait 2013) is a pure JavaScript 2D molecular editor that can export and import SMILES data.

Jmol (Hanson 2010) is a Java applet for interactive 3D visualization that provides significant cheminformatics support and a custom scripting language.

JSmol (Hanson 2013) is the JavaScript port of Jmol and does not require the Java plugin to run.

NGL (Rose 2015) is a WebGL-accelerated viewer and JavaScript library for interactive 3D visualization of macromolecules.

PV (Protein Viewer) (Biasini; 2015) is a WebGL-accelerated viewer for interactive 3D visualization of macromolecules with a functional-style API.


Quantitative Structure-Activity Relationship (QSAR) approaches find relationships between the chemical structures of a series of compounds (or structural-related properties) and a biological activity (Exploring {QSAR}), including ADMET properties (absorption, distribution, metabolism, excretion, and toxicity). QSAR methods calculate relevant molecular descriptors, build informative models using these descriptors and then apply the models. Models and datasets may also be visualized to aid in model development and understanding of compound libraries. Note that most of the cheminformatics toolkits shown in Table \ref{chemtool} also are capable of generating descriptors (and are often used as libraries by QSAR modeling software).

\label{descriptors} Open source software for computing molecular descriptors.
Name URL License Activity Citation
4D-FAP LGPL C2 (Jahn 2010)
BlueDesc GPL C3
MolSig GPL C2 (Carbonell 2013)
PaDEL-descriptor Public Domain C1 (Yap 2010)
TMACC GPL C2 (Melville 2007)
\label{qsarmodel} Open source software for building QSAR models.
Name URL License Activity Citation
AZOrange LGPL C2 (Stålring 2011)
Bioalerts GPL A3 (Cortes-Ciriano 2016)
camb GPL B2 (Murrell 2015)
eTOXlab GPL B3 (Carrió 2015)
Open3DQSAR GPL B1 (Tosco 2010)
QSAR-tools BSD A3
\label{qsarapply} Open source software that applies a QSAR model.
Name URL License Activity Citation
SMARTCyp LGPL C1 (Rydberg 2013)
Toxtree GPL A1 (Patlewicz 2008)
UG-RNN Apache C1 (Lusci 2013)
\label{qsarviz} Open source software for visualizing QSAR models and compound datasets.
Name URL License Activity Citation
CheS-Mapper GPL A2 (Gütlein 2014)
DataWarrior GPL A1 (Sander 2015)
DecoyFinder GPL A1 (Cereto-Massague 2012)
Scaffold Hunter GPL A1 (Wetzel 2009)
Synergy Maps MIT A2 (Lewis 2015)
VIDEAN BSD A3 (Martínez 2015)
WCSE BSD A2 (Ertl 2015)
WebChemViewer BSD C3 (Durrant 2014)

Descriptor Calculators (Table \ref{descriptors})

4D Flexible Atom-Pair Kernel (4D FAP) computes a ‘4D’ similarity measure from the molecular graphs of an ensemble of conformations which can be incorporated into QSAR models.

The BlueDesc descriptor calculator is a command-line tool that converts an MDL SD file into ARFF and LIBSVM format using CDK and JOELib2 for machine learning and data mining purposes. It computes 174 descriptors taken from both libraries.

MolSig (Carbonell 2013) computes molecular graph descriptors that include stereochemistry information.

PaDEL-Descriptor (Yap 2010) calculates molecular descriptors and fingerprints. It computes 1875 descriptors (1444 1D, 2D descriptors and 431 3D descriptors) and 12 types of fingerprints.

Topological maximum cross correlation descriptors (TMACC) (Melville 2007) generates 2D autocorrelation descriptors that are low dimensional and interpretable and appropriate for QSAR modeling.

Model Building (Table \ref{qsarmodel})

AZOrange (Stålring 2011) is a machine learning package that supports QSAR model building in a full work flow from descriptor computation to automated model building, validation and selection. It promotes model accuracy by using several high performance machine learning algorithms for efficient data set specific selection of the statistical approach.

Bioalerts (Cortes-Ciriano 2016) uses RDKit fingerprints to create models from discrete (e.g., toxic/non-toxic) and continuous data. It includes the capability to visualize problematic functional groups.

Chemistry aware model builder (camb) (Murrell 2015) is an R package for the generation of quantitative models. Its capabilities include descriptor calculation (including 905 two-dimensional and 14 fingerprint type descriptors for small molecules, 13 whole protein sequence descriptors, and 8 types of amino acid descriptors), model generation, ensemble modeling, and visualization.

eTOXLab (Carrió 2015) provides a portable modeling framework embedded in a self-contained virtual machine for easy deployment.

Open3DGrid and Open3DQSAR (Tosco 2010) are a suite of related tools that build 3D QSAR models. Open3DGrid generates molecular interaction fields (MIFs) in a variety of formats, and Open3DQSAR builds predictive models from the MIFs of aligned molecules. Calculations can be visualized in real time in PyMOL.

QSAR-tools is a set of Python scripts that use RDKit to build linear QSAR models from 2D chemical data.

Model Application (Table \ref{qsarapply})

SMARTCyp (Rydberg 2010) is a QSAR model that predicts the sites of cytochrome P450-mediated metabolism of drug-like molecules directly from the 2D structure of a molecule using fragment-based energy rules.

Toxtree (Patlewicz 2008) is a Java GUI application for estimating the “toxic hazard” of molecules using a variety of toxicity prediction modules, such as oral toxicity, skin and eye irritation prediction, covalent protein binding and DNA binding, Cytochrome P450-mediated drug metabolism (using SMARTCyp) and more.

UG-RNN/AquaSol (Lusci 2013) is an undirected graph recursive neural network that has been trained to predict aqueous solubility from molecular graphs.

Visualization (Table \ref{qsarviz})

CheS-Mapper (chemical space mapper) (Gütlein 2012, Gütlein 2014). is a 3D-viewer for small compounds in chemical datasets. It embeds a dataset into 3D space by performing dimensionality reduction on the properties of the compounds.

DataWarrior (Sander 2015) is a data visualization and analysis tool for chemical data with a rich set of available property calculations, similarity metrics, modeling capabilities, and data set representations.

DecoyFinder (Cereto-Massague 2012) provides a GUI for selecting a set of decoy compounds from a large library that are appropriate matches to a given set of actives.

Scaffold Hunter (Wetzel 2009) provides a Java-based GUI for visualizing the relationship between compounds in a dataset.

Synergy Maps (Lewis 2015) visualizes synergistic activity extracted from screens of drug combinations.

VIDEAN (visual and interactive descriptor analysis) (Martínez 2015) is a visual tool for iteratively choosing a subset of descriptors appropriate for predicting a target property with the aid of statistical methods.

WCSE (Wikipedia chemical structure explorer) (Ertl 2015) runs as a web application and provides a 2D interface for visualizing and searching for 2D molecules.

WebChemViewer (Durrant 2014) is an online viewer for viewing and interacting with lists of compounds and their associated data.

Quantum Chemistry

Quantum mechanics (QM) is increasingly accessible and relevant to molecular modeling and in silico drug design (Vivo 2011). Quantum chemistry models molecules at the level of their wave function and offers enhanced accuracy and detail compared to classical models at a substantial increase in computation time. Corresponding to the large diversity of solution methods for Schrödinger’s equation, there are many software packages for ab initio quantum chemistry, as well as programs for interpreting and visualizing the results of these calculations.

\label{qmhelp} Open source software for analyzing the results of quantum chemistry calculations.
Name URL License Activity Citation
FragIt GPL A2 (Steinmann 2012)
cclib LGPL A1 (Oboyle 2008)
GaussSum GPL A1 (Oboyle 2008)
Geac GPL A3
Nancy_EX GPL A2 (Etienne 2014)
orbkit GPL A2 (Hermann 2016)
\label{qmviz} Open source software for quantum chemistry visualization.
Name URL License Activity Citation
ccwatcher GPL B2
Gabedit BSD C1 (Allouche 2010)
J-ICE GPL A1 (Canepa 2010)
QMForge GPL A1
wxMacMolPlt GPL A1 (Bode 1998)

Ab initio Calcuation (Table \ref{abinitio})

ABINIT (Gonze 2009) can calculate the total energy, charge density and electronic structure of molecules and periodic solids with density functional theory (DFT) and Many-Body Perturbation Theory (MBPT), using pseudopotentials and a planewave or wavelet basis. ABINIT also can optimize the geometry, perform molecular dynamics simulations, or generate dynamical matrices, Born effective charges, and dielectric tensors and many more properties.

ACES (Lotrich 2008) performs calculations such as single point energy calculations, analytical gradients, and analytical Hessians, and is highly parallelized, including support for GPU computing. A focus of ACES is the use of MBPT and the coupled-cluster approximation to reliable treat electron correlation.

BigDFT (Genovese 2008, Mohr 2014, Mohr 2015) performs ab initio calculations using Daubechies wavelets and has the capability to use a linear scaling method. Periodic systems, surfaces and isolated systems can be simulated with the proper boundary conditions. It is included as part of ABINIT.

CP2K (Hutter 2013) performs simulations of solid state, liquid, molecular and biological systems. Its particular focus is massively parallel and linear scaling electronic structure methods and state-of-the-art ab-initio molecular dynamics (AIMD) simulations. It is optimized for the mixed Gaussian and Plane-Waves method using pseudopotentials and can run on parallel and on GPUs.

Dacapo is a total energy program that uses density functional theory. It can do molecular dynamics/structural relaxation while solving the Schrödinger equations. It has support for parallel execution and is used through the Atomic Simulation Environment (ASE) (Bahn 2002)

ErgoSCM (Rudberg 2011) is a quantum chemistry program for large-scale self-consistent field calculations. It performs electronic structure calculations using Hartree-Fock and Kohn-Sham density functional theory and achieves linear scaling for both CPU usage and memory utilization.

ERKALE (Lehtola 2012) is designed to compute X-ray properties, such as ground-state electron momentum densities and Compton profiles, and core (x-ray absorption and x-ray Raman scattering) and valence electron excitation spectra of atoms and molecules.

GPAW (Enkovaara 2010) is a DFT code that uses the projector-augmented wave (PAW) technique (Blöchl 1994, Kresse 1999) and integrates with the atomic simulation environment (ASE) (Bahn 2002).

HORTON (Helpful Open-source Research TOol for N-fermion systems) has as a primary design goal ease of extensibility for researching new methods in ab initio electronic structure theory.

JANPA (Nikolaienko 2014) computes natural atomic orbitals from a reduced one-particle density matrix.

MPQC (massively parallel quantum chemistry program) (Janssen 1995) offers many features including closed shell, unrestricted and general restricted open shell Hartree-Fock energies and gradients, closed shell, unrestricted and general restricted open shell density functional theory energies and gradients, second order open shell perturbation theory and Z-averaged perturbation theory energies.

NWChem (Valiev 2010) provides a full suite of methods for modeling both classical and QM systems. Its capabilities include molecular electronic structure, QM/MM, pseudopotential plane-wave electronic structure, and molecular dynamics and is designed to scale across hundreds of processors.

Octopus pervorms ab initio calculations using time-dependent DFT (TDDFT) and pseudopotentials. Included in the project is libxc (Marques 2012) which is a standalone library of exchange-correlation functionals for DFT (released under the LGPL).

OpenMX (Open source package for material eXplorer) (Ozaki 2005) is designed for nano-scale material simulations based on DFT, norm-conserving pseudopotentials, and pseudo-atomic localized basis functions. OpenMX is capable of performing calculations of physical properties such as magnetic, dielectric, and electric transport properties and is optimized for large-scale parallelism.

Psi4 (Turney 2011) is a suite of ab initio quantum chemistry programs that supports a wide range of computations (e.g., Hartree–Fock, MP2, coupled-cluster) and general procedures such as geometry optimization and vibrational frequency analysis with more than 2500 basis functions.

PyQuante is a collection of modules, mostly written in Python, for performing Hartree-Fock and DFT calculations with a focus on providing a well-engineered set of tools. A new version is under development (

PySCF is also written primarily in Python and supports several popular methods such as Hartree-Fock, DFT, and MP2. It also has easy of use and extension as primary design goals.

QMCPACK (Kim 2010) is a many-body ab initio quantum Monte Carlo implementation for computing electronic structure properties of molecular, quasi-2D and solid-state systems. The standard file formats utilized for input and output are in XML and HDF5.

QUANTUM ESPRESSO (Giannozzi 2009) is designed for modeling at the nanoscale using DFT, plane waves, and pseudopotentials and its capabilities include ground-state calculations, structural optimization, transition states and minimum energy paths, ab initio molecular dynamics, DFT perturbation theory, spectroscopic properties, and quantum transport.

RMG (Moore 2012) is a DFT code that uses real space grids to provide high scalability across thousands of processors and GPU acceleration for both structural relaxation and molecular dynamics.

Siam Quantum (SQ) is optimized for parallel computation and its capabilities include the calculation of Hartree-Fock and MP2 energies, minimum energy crossing point calculations, geometry optimization, population analysis, and quantum molecular dynamics.

Helper Applications (Table \ref{qmhelp})

FragIt (Steinmann 2012) generates fragments of large molecules to use as input files in quantum chemistry programs that support fragment based methods.

cclib (Oboyle 2008) provides a consistent interface for parsing and interpreting the results of a number of quantum chemistry packages.

GaussSum (Oboyle 2008) uses cclib to extract useful information from the results of quantum chemistry programs (ADF, GAMESS, Gaussian, Jaguar) including monitoring the progress of geometry optimization, the UV/IR/Raman spectra, molecular orbital (MO) levels and MO contributions.

Geac (Gaussian ESI Automated Creator) extracts data from Gaussian log files.

Nancy_EX (Etienne 2014) post-processes Gaussian output and analyzes excited states including natural transition orbitals, detachment and attachment density matrices, and charge-transfer descriptors.

orbkit (Hermann 2016) is a post-processing tool for the results of quantum chemistry programs. It has native support for a number of programs (MOLPRO, TURBOMOLE, GAMESS-US, PROAIMS/AIMPAC, Gaussian) and additionally interfaces with cclib for additional file format support. It can extract grid-based quantities such as molecular orbitals and electron density, as well as Muliken population charges and other properties.

Visualization (Table \ref{qmviz})

CCP1GUI provides a graphical user interface to various computational chemistry codes with an emphasis on integration with the GAMESS-UK quantum chemistry program.

ccwatcher provides a graphical interface for the monitoring of computational chemistry programs.

Gabedit (Allouche 2010) is a graphical user interface to a large number of quantum chemistry packages. It can create input files and graphically visualize calculation results.

J-ICE (Canepa 2010) is a Jmol-based viewer for crystallographic and electronic properties that can be deployed as a Java applet embedded in a web browser.

QMForge provides a graphical user interface for analyzing and visualizing results of quantum chemistry DFT calculations (Gaussian, ADF, GAMESS, Jaguar, NWChem, ORCA, QChem). Analyses include a number of population analyses, Mayer’s bond order, charge decomposition, and fragment analysis.

wxMacMolPlt (Bode 1998) is a multi-platform GUI for setting up and visualizing input and output files for the GAMESS quantum chemistry software.

Ligand Dynamics and Free Energy Calculations

Simulation based approaches for analyzing ligand-protein interactions have the potential to fully account for the dynamics of the ligand, protein, and water system. Molecular dynamics simulation is a powerful tool for computing free energies as long as the system can be properly parameterized and the simulation code adapted and analyzed to implement the desired free energy method.

Simulation Software (Table \ref{mdtable})

Campari (Vitalis 2009) conducts flexible Monte Carlo sampling of biopolymers in internal coordinate space, with built-in analysis routines to estimate structural properties and support for replica exchange and Wang-Landau sampling.

DL_POLY Classic (Smith 2002) is a general purpose molecular dynamics simulation package that can run in parallel and includes a Java graphical user interface.

GALAMOST (GPU accelerated large-scale molecular simulation toolkit) (Zhu 2013) uses GPU computing to perform traditional molecular dynamics with a special focus on polymeric systems at mesoscopic scales.

Gromacs (Pronk 2013) is a complete and well-established package for molecular dynamics simulations that provides high performance on both CPUs and GPUs. It can be used for free energy and QM/MM calculations and includes a comprehensive set of analysis tools.

Iphigenie (Lorenzen 2012) is a molecular mechanics program that features polarizable force fields, the HADES reaction field, and QM/(P)MM hybrid simulations.

LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) (Plimpton 1995) is a highly modular classical molecular dynamics simulator that includes a diverse array of energy potentials and integrators.

MDynaMix (Lyubartsev 2000) is a basic general purpose molecular dynamics package.

MMTK (Molecular Modelling Toolkit) (Hinsen 2000) is a library written in Python (with some time critical parts written in C) for constructing and simulating molecular systems. Its capabilities include molecular dynamics, energy minimization, and normal mode analysis and it is well-suited for methods development.

OpenMM (Eastman 2013) is a substantial toolkit for high performance molecular dynamics simulations that includes support for GPU acceleration.

ProtoMol (Matthey 2004), and the associated MDLab Python bindings (Cickovski 2009), provides an object-oriented framework for prototyping algorithms for molecular dynamics simulations and includes an interface to OpenMM.

ProtoMS (Michel 2006) is a Monte Carlo biomolecular simulation program which can be used to calculate relative and absolute free energies and water placement with the GCMC and JAWS methodologies.

Sire is a collection of modular libraries intended to facilitate fast prototyping and the development of new algorithms for molecular simulation and molecular design. It has apps for system setup, simulation, and analysis.

WESTPA (The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis) (Zwier 2015) is a library for performing weighted ensemble simulations to sample rare events and compute rigorous kinetics.

yank is built off of OpenMM and provides a Python interface for performing alchemical free energy calculations.

Simulation Setup and Analysis (Table \ref{mdanalysis})

AmberTools (Salomon-Ferrer 2012) is an open source component of the non-open source Amber package and provides a large suite of analysis programs. As of Amber15, AmberTools includes the lower performance, but readily extendable, sander molecular dynamics code.

LOOS (Lightweight Object-Oriented Structure library) (Romo 2014) is a C++ library (with Python bindings) for reading and analyzing molecular dynamics trajectories that also includes a number of standalone programs.

lsfitpar (Vanommeslaeghe 2015) derives bonded parameters for Class I force fields by performing a robust fit to potential energy scans provided by the user.

MDAnalysis (Michaud-Agrawal 2011) is a Python library for reading and analyzing molecular dynamics simulations with some time critical sections written in C.

MDTraj (McGibbon 2015) provides high-performance reading, writing, and analysis of molecular dynamics trajectories in a diversity of formats from a Python interface.

MEMBPLUGIN (Guixà-González 2014) analyzes molecular dynamics simulations of lipid bilayers and is most commonly used as a VMD plugin.

MEPSA (Minimum Energy Pathway Analysis) (Marcos-Alcalde 2015) provides tools for analyzing energy landscapes and pathways.

MSMBuilder (Beauchamp 2011) is an application and Python library for building Markov models of high-dimensional trajectory data.

packmol (Martínez 2009) is a utility for setting molecular systems by realistically packing molecules to obey a variety of constraints and can create solvent mixtures and lipid bilayers.

PDB2PQR (Dolinsky 2007) prepares structures for electrostatics calculations by adding hydrogens, calculating sidechain pKa, adding missing heavy atoms, and assigning force field-dependent parameters; users can specify an ambient pH.

PLUMED (Tribello 2014) interfaces with an assortment of molecular dynamics software packages to provide a unified interface for performing free energy calculations using methods such as metadynamics, umbrella sampling and steered MD (Jarzynski).

ProDy (Bakan 2011) is a Python toolkit for analyzing proteins and includes facilities for trajectory analysis and druggability predictions using simulations of molecular probes (Bakan 2012).

Pteros (Yesylevskyy 2015) is a C++ library (with Python bindings) for reading and analyzing molecular dynamics trajectories.

PyEMMA (Scherer 2015) is a Python library for performing kinetic and thermodynamic analyses of molecular dynamics simulations using Markov models.

PyRED (Dupradeau 2010) generates RESP and ESP charges for the AMBER, CHARMM, OPLS, and Glycam and force fields.

PYTRAJ is a Python interface to the cpptraj tool of AmberTools.

simpletraj is a lightweight Python library for parsing molecular dynamics trajectories.

WHAM (Weighted Histogram Analysis Method) calculates the potential of mean force (PMF) from umbrella sampling simulations.

Virtual Screening and Ligand Design

The goal of virtual, or in silico, screening is to computationally identify small molecules in a compound library that are active against a given target. Virtual screening methods usually adopt either a ligand-based approach, where properties of known active compounds are used to identify additional compounds, or a structure-based approach, where the interactions between putative ligands and the receptor structure are used. Tools for chemical similarity, which can be used for ligand-based screening, are cataloged in the Cheminformatics section.

In contrast to virtual screening, which evaluates predetermined compounds, de novo ligand design attempts to create a molecule ‘from scratch’ that binds to a protein. Methods differ in how they specify the objective to optimize (e.g., docking score to a protein) and how candidate molecules are created, where a key challenge is maintaining synthetic accessibility.

\label{ligandtable} Open source software for ligand-based virtual screening.
Name URL License Activity Citation
ACPC BSD B2 (Berenger 2014)
Align-it LGPL C3
Open3DALIGN GPL B2 (Tosco 2011)
PAPER GPL C2 (Haque 2010)
Pharmer GPL B1 (Koes 2011)
Pharmit GPL A3 (Sunseri 2016)
Shape-it LGPL C3
USRCAT MIT C2 (Schreyer 2012)

Ligand-Based (Table \ref{ligandtable})

ACPC (AutoCorrelation of Partial Charges) (Berenger 2014) computes ligand similarity based on a rotation and translation invariant electrostatic descriptor.

Align-itTM is a successor of Pharao (Taminau 2008) and aligns and scores 3D representations of molecules based on their pharmacophore features. It includes a plugin for integration with PyMOL.

Open3DALIGN (Tosco 2011) performs unsupervised rigid-body molecular alignment.

PAPER (Haque 2010) performs GPU accelerated alignment of molecular shapes using Gaussian overlays.

Pharmer (Koes 2011) uses efficient data structures to rapidly screen large libraries for ligand conformations that match a pharmacophore.

Pharmit (Sunseri 2016) is a successor of Pharmer that also incorporates shape matching and energy minimization (if a receptor structure is available) as part of the screen. It is primarily intended to be used as a backend to a web service.

Shape-itTM uses Gaussian volumes to align and score molecular shapes.

USRCAT (Schreyer 2012) performs “ultra-fast shape recognition” with the addition of pharmacophoric information to rapidly screen compound libraries for similar molecules.

Docking and Scoring (Table \ref{structable})

ADplugin is a plugin for PyMOL for interfacing with AutoDock and AutoDock Vina.

APBS (Baker 2001) performs solvation free energy calculations using the Poisson-Boltzmann implicit solvent method.

AutoDock (Morris 2009) is an automated docking program that uses a physics-based semiempirical scoring function (Huey 2007) mapped to atom type grids to evaluate poses and a genetic algorithm to explore the conformational space. It includes the ability to incorporate sidechain flexibility and covalent docking.

AutoDock Vina (Trott 2009) is an entirely separate code base and approach from Autodock that was developed with a focus on runtime performance and ease of system setup. It uses a fully empirical scoring function and an iterated local search global optimizer to produce docked poses. It includes support for multi-threading and flexible sidechains.

Clusterizer-DockAccessor (Ballante 2016) are tools for accessing the quality of docking protocols. It interfaces with a number of open source and free tools.

DockoMatic (Bullock 2013) provides a graphical user interface for setting up and analyzing AutoDock and AutoDock Vina docking jobs, including when run on a cluster. It also includes the ability to run inverse virtual screens (find proteins that bind a given ligand) and support for homology model construction.

DOVIS (Jiang 2008) is an extension of AutoDock 4.0 that provides more efficient parallelization of large virtual screening jobs.

idock (Li 2012) is a multi-threaded docking program that includes support for the AutoDock Vina scoring function and a random forest scoring function. I can output per-atom free energy information for hotspot detection.

MOLA (Abreu 2010) is a pre-packaged distribution of AutoDock and AutoDock Vina for deployment on multi-platform computing clusters.

NNScore (Durrant 2011) uses a neural network model to score protein-ligand poses.

Paradocks (Meier 2010) is a parallelized docking program that includes a number of population-based metaheuristics, such as particle swarm optimization, for exploring the space of potential poses.

PyRx (Dallakyan 2014) is a visual interface for AutoDock and AutoDock Vina that simplifies setting up and analyzing docking workflows. Its future as an open-source solution is in doubt due to a recent shift to commercialization.

rDock (Ruiz-Carmona 2014) is designed for docking against proteins or nucleic acids and can incorporate user-specified constraints. It uses an empirical scoring function that includes solvent accessible surface area terms. A combination of genetic algorithms, Monte Carlo, and simplex minimization is used to explore the conformational space. Distinct scoring functions are provided for docking to proteins and nucleic acids.

RF-Score (Li 2015, Ballester 2010) uses a random forest classifier to score protein-ligand poses.

smina (Koes 2013) is a fork of AutoDock Vina designed to better support energy minimization and custom scoring function development (scoring function terms and atom type properties can be specified using a run-time configuration file). It also simplifies the process of setting up a docking run with flexible sidechains.

VHELIBS (Validation HElper for LIgands and Binding Sites) (Cereto-Massagué 2013) assists the non-crystallographer in validating ligand geometries with respect to electron density maps.

VinaLC (Zhang 2013) is a fork of AutoDock Vina designed to run on a cluster of multiprocessor machines.

VinaMPI (Ellingson 2013) is a wrapper for AutoDock Vina that uses OpenMPI to run large-scale virtual screens on a computing cluster.

Zodiac (Zonta 2008) is a visual interface for structure-based drug design that includes support for haptic feedback.

Pocket Detection (Table \ref{pockettable})

eFindSite (Brylinski 2013) using homology modeling and machine learning predicts ligand binding sites in a protein structure.

fpocket (Schmidtke 2011) detects and delineates protein cavities using Voronoi tessellation and is able to process molecular dynamics simulations.

HOLLOW (Ho 2008) interfaces with PyMOL to produce clean visualizations of pockets and channels.

KVFinder (Oliveira 2014) is a PyMOL plugin for identifying and characterizing pockets.

mcvol (Till 2009) calculates protein volumes and identifying cavities using a Monte Carlo algorithm.

PAPCA (PocketAnalyzerPCA) is a pocket detection utility designed to analyze ensembles of protein conformations.

PCS (Pocket Cavity Search) measures the volume of internal cavities and evaluates the environment of ionizable residues.

PocketPicker (Weisel 2007) is a PyMOL plugin that automatically identifies potential ligand binding sites using a grid-based shape descriptor.

POVME (POcket Volume MEasurer) (Durrant 2014) is a tool for measuring and characterizing pocket volumes that includes a graphical user interface.

\label{denovotable} Open source software for ligand design.
Name URL License Activity Citation
AutoClickChem GPL C2 (Durrant 2012)
AutoGrow GPL A1 (Durrant 2013)
igrow Apache A3
OpenGrowth GPL A1 (Chéron 2015)

Ligand Design

AutoClickChem (Durrant 2012) performs in silico click chemistry to generate large libraries of synthetically accessible compounds.

AutoGrow (Durrant 2013) uses a genetic algorithm to explore the space of reactants and reactions accessible via AutoClickChem and identifies compounds that dock well using AutoDock Vina.

igrow, like AutoGrow, uses a genetic algorithm but transforms ligands using branch exchange and uses idock as the underlying docking evaluation protocol.

OpenGrowth (Chéron 2015) assembles candidate ligands by connecting small organic fragments in the active site of proteins. It includes a graphical user interface.


Figure 1: Distribution of open source licenses used in cataloged software packages. \label{pies}\label{usage}\label{develop}{subfigure}

[t].4 {subfigure}[t].4

Figure 2: Figure 3: Figure 4: Activity distributions of cataloged software packages. (\subrefdevelop) Distribution of development activity. (\subrefusage) Distribution of user activity. Figure 5: Distribution of citations as reported by Google Scholar generated on average every year by those software packages with citeable publications.

We have cataloged over 200 open-source packages for molecular modeling that provide a wide range of capabilities. As shown in Figure \ref{licenses}, the most popular license (55%) is some variant of the copyleft GNU Public License, which ensures that derivative works remain open source. Interestingly 78% of the packages cataloged have a corresponding citeable publication which suggests that much of the software originates from academia. The distribution of average citations generated a year (as reported by Google Scholar) for the citeable publications is shown in Figure \ref{cites}. A significant majority (84%) of publications generate at least one citation a year, 29% generate at least 10 citations, and 8% generate more than 100 citations a year on average.

A substantial portion of the packages cataloged are under active development and see significant usage, as shown in Figure \ref{pies}. We rated 56% of the packages as ‘A’ level development, meaning major features or releases were made within the last 18 months, and 51% see substantial usage (rank 1). There are a number of projects (30%) where development has apparently ceased (no changes within the last 18 months). Note that our methodology for identifying packages ignores cases where software is no longer available, this is an underestimate. However, although we did find instances where an open source package was referenced in a paper but was no longer available, we did not find this to be a common occurrence. Most packages, even those that have remained unchanged for a decade, see some usage. In fact, a number of packages (23), still see significant usage despite having received no development for the past 18 months. This underlies the importance of releasing source code through a third-party site such as SourceForge or GitHub as it ensures the continued existence of a project.

A major advantage of open source is that in cases where a popular project is not being actively developed (e.g. AutoDock Vina (Trott 2009)) new projects can fork the source code and continue development (e.g. smina (Koes 2013)). However, a potential problem area with open source development is the lack of central coordination and efficient pooling of resources. For example, there are several forks of AutoDock Vina that improve it’s performance on computing clusters and there are an array of tools in several categories that effectively perform the same task. This underscores the importance of efforts like Blue Obelisk (Guha 2006, OBoyle 2011) and Open Chemistry ( which foster collaboration among open source cheminformatics projects.

It is clear that open source software plays an important role in the scientific community and is a vibrant sub-community of its own with a wide assortment of projects under development and in widespread use. The open source software packages cataloged here provide launching points for the development of new tools for enabling further scientific discovery.


We are grateful to Twitter users @DrBostrom, @KirkD_CO, @wojcikowskim, @egonwillighagen, @macinchem, @rguha, @ghutchis, @janhjensen, @rmcgibbo, @synapticarbors, and @khinsen for their helpful feedback on a draft of this manuscript. This work was supported by the National Institute of Health [R01GM108340].


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