Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159Open AccessS O F T W A R E
SoftwareCDK-Taverna: an open workflow environment for
cheminformatics
Thomas Kuhn1, Egon L Willighagen2, Achim Zielesny1 and Christoph Steinbeck*3
Abstract
Background: Small molecules are of increasing interest for bioinformatics in areas such as metabolomics and drug
discovery. The recent release of large open access chemistry databases generates a demand for flexible tools to
process them and discover new knowledge. To freely support open science based on these data resources, it is
desirable for the processing tools to be open source and available for everyone.
Results: Here we describe a novel combination of the workflow engine Taverna and the cheminformatics library
Chemistry Development Kit (CDK) resulting in a open source workflow solution for cheminformatics. We have
implemented more than 160 different workers to handle specific cheminformatics tasks. We describe the applications
of CDK-Taverna in various usage scenarios.
Conclusions: The combination of the workflow engine Taverna and the Chemistry Development Kit provides the first
open source cheminformatics workflow solution for the biosciences. With the Taverna-community working towards a
more powerful workflow engine and a more user-friendly user interface, CDK-Taverna has the potential to become a
free alternative to existing proprietary workflow tools.
Background
Small molecules are of increasing interest for bioinformat-
ics in areas such as metabolomics and drug discovery. The
recent release of large open chemistry databases into the
public domain [1-4] calls for flexible, open toolkits to pro-
cess them. These databases and tools will, for the first time,
create opportunities for academia and third-world countries
to perform state-of-the-art open drug discovery and transla-
tional research - endeavors so far a domain of the pharma-
ceutical industry. Commonly used in this context are
workflow engines for cheminformatics, where numerous
recurring tasks can be automated, including tasks for
• chemical data filtering, transformation, curation and
migration workflows
• chemical documentation and information retrieval
related workflows (structures, reactions, pharmacoph-
ores, object relational data etc.)
• data analysis workflows (statistics and clustering/
machine learning for QSAR, diversity analysis etc.)
The workflow paradigm allows scientists to flexibly cre-
ate generic workflows using different kinds of data sources,
filters and algorithms, which can later be adapted to chang-
ing needs. In order to achieve this, library methods are
encapsulated in Lego™-like building blocks which can be
manipulated with a mouse or any pointing device in a
graphical environment, relieving the scientist from the need
to learn a programming language. Building blocks are con-
nected by data pipelines to enable data flow between them,
which is why pipelining is often used interchangeably for
workflow. Workflows are increasingly used in cheminfor-
matics research [5,6].
Existing proprietary or semi-proprietary implementa-
tions of the workflow or pipeline paradigm in molecular
informatics include Pipeline Pilot [7] from SciTegic, a sub-
sidiary of Accelrys or the InforSense platform from InforS-
ense [8]. Both are commercially well established but closed
source products with a large variety of different functional-
ity. KNIME [9] is a modular data exploration platform
which uses a dual licensing model with the Aladdin free
public license. It is developed by the group of Michael
Berthold at the University of Konstanz, Germany. KNIME* Correspondence: steinbeck@ebi.ac.uk
3© 2010 Kuhn et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
is based on the open source Eclipse platform. An overview Chemoinformatics and Metabolism, European Bioinformatics Institute (EBI),
Cambridge, UK
Full list of author information is available at the end of the article

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 2 of 11
of workflow systems in life sciences was recently given by
Tiwari et al. [10].
In 2005 we started to integrate our open source chemin-
formatics library, the Chemistry Development Kit (CDK)
[11,12] with Taverna [13-15], a workflow environment with
an extensible architecture, to produce CDK-Taverna, the
first completely free [16] workflow solution for cheminfor-
matics, which we present here.
It makes additional use of other open source components
such as Bioclipse [17] for visualization of workflow results,
and Pgchem::tigress [18] as an interface to the database
back-end for storage of large data sets.
Implementation
The here introduced CDK-Taverna plugin takes advantage
of the plug-in detection manager of Taverna for its installa-
tion. This manager requires a plug-in description XML file
containing a plug-in name, a version number, a target Tav-
erna version number, a repository location and a Maven-
like Java package description, all provided by the plug-in's
installation website: http://www.cdk-taverna.de/plugin/.
After adding this URL, the manager presents all available
plug-in versions graphically to the user. In order to install
the CDK-Taverna plug-in the user selects the desired ver-
sion after which all necessary Java libraries are installed on-
the-fly from the given installation website.
The CDK-Taverna plug-in is written in Java is published
under the GNU Lesser General Public License (LGPL).
Version 0.5.1.1 uses CDK revision 12084. Like Taverna
itself the CDK-Taverna plug-in uses Maven 2 [19] as a
build system.
To integrate the CDK functionality, the plug-in makes use
of the extension points provided by Taverna allowing
dynamic discovery of the provided functionality. The fol-
lowing sections describe what extension points are used,
and how molecular data is represented when flowing
through the workflow.
Taverna's extension points
Taverna allows the execution of workflows linking together
heterogeneous open services, applications or databases
(remote or local, private or public, third-party or home-
grown) [20]. For the integration of these different resource
types Taverna provides various interfaces and protocols for
its extension. For example, it allows for easy access to web-
services through WSDL [21] and SOAP [22].
The CDK-Taverna plug-in, on the other hand, uses Tav-
erna's local extension mechanism. For local extensions,
Taverna provides a list of different Service Provider Inter-
faces (SPI), as given in Table 1. CDK-Taverna implements
several of these, integrating CDK functionality as so-called
in's source code is available at http://cdk.sourceforge.net/
cdk-taverna/api/.
All workers in CDK-Taverna implement the CDKLocal-
Worker interface. It is used for the detection of workers by
the CDKScavenger class which itself implements the Tav-
erna SPI org.embl.ebi.escience.scu-
flui.workbench.Scavenger interface. Adding user
interfaces for some of the workers requires an extension of
the AbstractCDKProcessorAction which again
implements the Taverna SPI
org.embl.ebi.escience.scuflui.spi.Pro-
cessorActionSPI. The use of this SPI allows the addi-
tion of, for example, file chooser dialogs for workers like
file reader or writer.
The anatomy of a CDK-Taverna worker
To create a CDK-Taverna worker the Java class of this
Table 1: List of available Service Provider Interfaces that
can be used to create plug-ins for Taverna to provide
additional functionality.
Interfaces
org.embl.ebi.escience.scuflworkers.java.LocalWorker
net.sf.taverna.perspectives.PerspectiveSPI
org.embl.ebi.escience.scuflui.spi.ProcessorActionSPI
org.embl.ebi.escience.scuflworkers.ProcessorInfoBean
org.embl.ebi.escience.scuflui.spi.RendererSPI
org.embl.ebi.escience.scuflui.spi.ResultMapSaveSPI
org.embl.ebi.escience.scuflui.workbench.scavenger.spi.Scaveng
erActionSPI
org.embl.ebi.escience.scuflworkers.ScavengerHelper
org.embl.ebi.escience.scuflui.workbench.Scavenger
org.embl.ebi.escience.scuflui.actions.ScuflModelActionSPI
org.embl.ebi.escience.scuflui.spi.UIComponentFactorySPILocal Workers which run on the same machine as the Tav-
erna installation. Full JavaDoc documentation of the plug- worker has to implement the CDKLocalWorker Interface.

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 3 of 11
This interface defines that every worker has to define the
following methods:
public Map <String, DataThing> exe-
cute(Map String, DataThing inputMap)
throws TaskExecutionException;
public String[] inputNames();
public String[] inputTypes();
public String[] outputNames();
public String[] outputTypes();
The method inputNames and outputNames return the
names of the ports of each worker whereas the inputTypes
and outputTypes methods return the names for the Java
object types with its package declaration e.g. java/
java.util.List for a List. Within CDK-Taverna
chemical structures are passed around using the Java object
java/org.openscience.cdk.applica-
tions.taverna.CMLChemfile
Results
The CDK-Taverna plug-in currently provides 164 different
cheminformatics workers. The fields of application of these
workers are described in Table 2. These include workers for
input and output (I/O) of various chemical files and line
notations formats, databases, and descriptors for atoms,
bonds and molecules. The miscellaneous workers are e.g. a
substructure filter, an aromaticity detector, an atom typer or
a reaction enumerator. Some of the workers are outlined as
part of example workflows described below (for a complete
list see [23] or http://cdk.sourceforge.net/cdk-taverna/work-
ers.html). In the following, we outline the application of
CDK-Taverna with selected workflow scenarios. A larger
list of workflows is available from MyExperiment.org [24].
Iteration over large data sets
Cheminformatics by definition deals with the discovery of
chemical knowledge from large data collections. Because
these data sources are usually too large to be loaded into
memory as a whole, it is needed to loop over all data entries
to process them one by one. Unfortunately, the architecture
of Taverna 1.7 does not support such loops. CDK-Taverna,
therefore, provides workers which act like FOR or WHILE
loops, making use of Taverna's iteration-and-retry mecha-
nism to allow workflows to process large data sets.
Database I/O
For database support the CDK-Taverna project uses the
PostgreSQL [25] relational database management system
(RDBMS) with the open source Pgchem::tigress [18] exten-
sion. This combination allows storage and fast retrieval of
up to a million molecules without running into memory
limitations. The Pgchem::tigress extension uses an imple-
mentation of the Generalized Search Tree (GiST) [26] of
the PostgreSQL database. CDK-Taverna can use a local
installation of the PostgreSQL Database with the
Pgchem::tigress extension or can connect to a remote
instance.
Table 2: The worker allocation of CDK-Taverna by function.
Workers by function Number of workers Examples
File I/O 15 SDFParser, CML Reader & Writer
SMILES tools 2 SMILES Parser, SMILES Writer
InChI parser 2 InChI Parser, InChI Generator
Database I/O 7 Insert Molecules Into Database, Read
Molecules From Database
Molecular descriptors 42 AtomCount & LargestChain
Atom descriptors 27 AtomHybridization & BondsToAtom
Bond descriptors 6 PartialPiCharge
Clustering 13 K-Means, ART 2-A ClassificationMiscellaneous 50 Substructure Search, Reaction
Enumeration

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 4 of 11
Scenario 1: Substructure Search
We may want to design workflows for performing substruc-
ture searches in different ways depending on the type of
input. In a first example the substructure workflow per-
forms a topological substructure search on a list of given
molecules and a given molecular substructure (see Figure
1). The workflow inputs are a molecular substructure repre-
sented in the SMILES line notation [27] and a list of struc-
tures stored in a MDL SDfile [28]. The structures which
match the substructure are stored as MDL Molfiles [28].
The non-matching structures are converted into the Chemi-
cal Markup Language (CML) file format [29-31]. This
small example workflow already combines four different
molecular structure representations and the use of a topo-
logical substructure filter.
A related workflow performs a substructure search
directly on a database: It uses functionality provided by the
Pgchem extension of the PostgreSQL database. This exten-
sion allows the use of SQL commands to perform a sub-
structure search. A demonstration is given with the
workflow in Figure 2. The molecules containing the sub-
structure are reported in tabular form in a PDF file.
Scenario 2: Descriptor Calculation
The descriptor calculation workflow, depicted in Figure 3,
starts with loading its molecules from a PostgreSQL data-
base. The recognition of the atom types is the next step, fol-
lowed by the addition of implicit hydrogens for each
molecule as well as the detection of Hückel aromaticity.
Each molecule is tagged for the descriptor calculation pro-
cess. The tagging of the molecules is used to add a univer-
sal identifier to each molecule. This allows the
identification of the corresponding Quantitative Structure-
Figure 2 Workflow performing a substructure search (Scenario 1)
in a database [36]. The substructure is defined with a SMILES string.
The output is a PDF file with a tabular view of the molecules from the
database containing the substructure.Figure 1 Workflow performing a topological substructure search
(Scenario 1) on molecules from a MDL SDfile [35]. The input of this
workflow is a SMILES string which represents the substructure.
Figure 3 Workflow calculating various QSAR descriptors (Scenar-
io 2) for molecules from a PostgreSQL database. The results of the
calculation are stored in a CSV file.

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 5 of 11
Activity Relationship (QSAR) descriptor values within the
table of calculated descriptor values. The QSAR worker
provides a user interface based selection of multiple QSAR
descriptors (see Figure 4) for the calculation of a molecule's
property vector based on the CDK.
The result of the workflow is a comma separated value
(CSV) text file which contains the ID of the molecule and
the calculate property values. The property vectors may
then be used for statistical analysis, clustering or machine
learning purposes. With the PostgreSQL Database back-end
CDK-Taverna is able to calculate a large number of
descriptors for many thousand of molecules in a reasonable
time (see Figure 5).
Scenario 3: Iterative Descriptor Calculation
The iterative descriptor calculation workflow is a work-
around which allows the treatment of hundreds of thou-
sands of molecules. This workflow (see Figure 6) processes
each molecule in the same manner as the non-iterative
descriptor calculation workflow but it uses different data-
base workers. Instead of the single database worker
Get_Molecules_From_Database three database
workers are applied:
Iterative_Molecule_From_Database_Reader,
Get_Molecule_From_Database and
Has_Next_Molecule_From_Database. The first of
the three workers is used to configure the database connec-Figure 4 User interface to select QSAR descriptors to be calculated for each molecule during the execution of the descriptor calculation
workflow shown in Figure 3.

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 6 of 11
tion and store it within an internal object registry. The sec-
ond worker gets the ID of the database connection as an
input and loads molecules from the database. Only a subset
of the original query is loaded using the SQL functions
LIMIT and OFFSET. The last database worker checks
whether the set of loaded molecules is the last of this query
or if further molecules must be loaded. If the latter applies
the output of this last worker would be the text value true. A
last but essential worker is Fail_if_true. This worker
throws an exception if it gets the value true as input. This
worker is crucial for the nested workflow: If it fails the
whole nested workflow fails. Taverna then provides a retry
mechanism for a failing worker or nested workflow. This
mechanism is used to re-run the nested workflow as often
as necessary. This dirty workaround might become obsolete
in later versions of Taverna.
Scenario 4: Validation of CDK Atom Types
The calculation of physicochemical properties in the Chem-
istry Development Kit (CDK) relies on the detection of
atom types for all atoms in each molecule. The atom types
describe basic atomic properties needed by the various
cheminformatics algorithms implemented in the CDK. If no
atom type is recognized for an atom, the atom is flagged as
detection of an unknown atom type by the CDK indicates
that either the CDK lacks this specific atom type or the mol-
ecule contains chemically nonsensical atom types. In Fig-
ure 7 the Perceive_atom_types worker performs an
atom type detection, followed by the retrieval of the data-
base ID for those molecules with unknown atom type by the
Extract_the_databaseID_from_the_molecul
e worker. The workflow creates two text files, one contain-
ing the identifier of all molecules with unknown atom
types, created by the Iterative_File_Writer, and a
second one containing information about which atom of
which molecule is unknown to the CDK. An analysis [23]
of the atom type detection was performed on three different
databases, two proprietary natural products databases and
the open access database of Chemical Entities of Biological
Interest (ChEBI) [32,33], maintained at the European Bio-
informatics Institute (EBI). The workflow was run with
more than 600 thousand molecules and showed that the
CDK algorithms matches the atom types quite well, but that
the atom type list is not complete for metals and other
heavy atoms (see Figure 8). Missing atom type definitions
is a general problem to many cheminformatics algorithms
and not unique to the CDK: it leads to severe problems and
Figure 5 Overview of the time needed to calculate different molecular descriptors for 1000 molecules [37].unknown. Based on the CDK's atom type perception func-
tionality, we devised an example workflow (see Figure 7)
for the validation of the CDK atom typing procedures. The
computation error. Therefore, initial atom type perception is
an important filter for cheminformatics workflows.

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 7 of 11
Scenario 5: Reaction Enumeration
Markush structures are chemical drawings which represent
a series of molecules by indicating locations where differ-
ences occur. These locations are marked as Heterocyclic,
Alkyl, or identified by an R group, enumerating a series of
possible groups, such as Methyl, Isopropyl, and Pentyl.
Markush structures are commonly used in patents for
describing whole compound classes and are named after
In the process of reaction enumeration, Markush struc-
tures are used to design generic reactions. These reactions
are usable for the enumeration of large chemical spaces,
which includes the generation of chemical target libraries.
The results of the enumeration have important applications
in patent formulation and in High Throughput Screening
(HTS). HTS experiments screen large amounts of small
molecules, called molecule libraries, against one or more
assays for testing for biological activity. A couple of years
ago, the libraries used for a single HTS experiment con-
sisted of up to 100.000 molecules. Nowadays, more tar-
geted libraries of a reduced size of up to 1.000 molecules
are used, but still commonly defined using Markush struc-
tures.
For reaction enumeration, a given reaction contains dif-
ferent building blocks, which are needed for the enumera-
tion. Each reactant of the reaction represents a building
block. A scientist then selects a number of molecules for
each reactant and the reaction enumeration creates a list of
all possible products. The list of products then passes a vir-
tual screening before at last a scientist decides which prod-
ucts will be synthesized. Results can be visualized and
inspected at the end in Bioclipse [17]. CDK-Taverna con-
tains workers which support an enumeration task based on a
generic reaction (see Figures 9 and 10).
Scenario 6: Clustering Workflows
During the work on this project the majority of workflows
used unsupervised clustering with an implementation of the
ART 2-A algorithm [34]. This algorithm was chosen
because of its capability to automatically cluster open-cate-
Figure 6 Workflow iteratively calculating different QSAR descrip-
tors (Scenario 3) for molecules loaded from a PostgreSQL data-
base [38]. The results are stored in a CSV file.
Figure 7 Workflow for iterative loading of molecules from a data-
base and searches for molecules with atom types unknown to the
Chemistry Development Kit (Scenario 4).Eugene A. Markush who described these kind of structures
firstly in his US patent in the 1920s. gorical problems. Compared to traditional clustering meth-
ods like the k-means, the ART 2-A algorithm is
computationally less demanding and therefore applicable

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 8 of 11
Figure 8 Allocation of the unknown atom types detected during the analysis of the ChEBI database (12367 molecules). A total of 2414 atoms
in 1035 molecules (8.36%) did not have a recognized atom type. X1 summarizes unrecognized atom types for the elements Am, Cf, Cm, Dy, Es, Fm,
Ga, Lr, Md, Na, Nb, No, Np, Pm, Pu, Sm, Tb, Tc, Th, and Ti.

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 9 of 11
especially to large data sets. Within a typical clustering
workflow the Get_QSAR_vector_from_database
worker loads molecule's data vectors (compare descriptor
calculation workflow above) for a specific molecular SQL
query from the database. This worker provides options to
inspect the result vector which includes checks for values
such as Not a Number (NaN) or Infinity. In addition, differ-
ent thresholds may be specified for components or com-
plete vector removal, e.g. for the removing of components
whose minimum value equals its maximum value. After the
loading and cleaning of a data vector, the clustering task is
performed using the ART2A_Classificator worker,
as depicted in Figure 11. For the configuration of this
worker different options are available:
• linear scaling of the input vector to values between 0
and 1,
• a switch between deterministic random and random
random for the selection of the vectors to process,
• the definition of the convergence criteria of the clus-
tering process,
• the required similarity for the convergence criteria,
• the maximum clustering time, and
• a limit for the number of clustering steps and a range
for the vigilance parameter that guides the ART 2-A
algorithm.
The implemented ART 2-A algorithm contains two possi-
ble convergence criteria. It converges if the classification
does not change after one epoch or if the scalar product of
the classes between two epochs is less than the required
similarity. The clustering result is stored in form of a com-
pressed XML document. This XML result document can be
Figure 9 Reaction enumeration (Scenario 5) loading a generic re-
action from a MDL RXNfile and two reactant lists from MDL SD-
files. The products from the enumeration are stored as MDL Molfiles.
Besides these files a PDF document showing the 2D structure of the
products is created. At the end Bioclipse will start up to allow visualiza-
tion and analysis of the results [39].Figure 10 Reaction enumeration example with two building blocks. For each building block, a list of three reactants is defined. This enumeration
results in nine different products.

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 10 of 11
processed with different workers to create different visual-
izations depending on the aim of the clustering task. For
chemical diversity analysis the ART 2-A worker was used
for a successive top-down clustering of three chemical data-
bases (two proprietary databases containing natural prod-
ucts and the ChEBI database [33] containing molecules of
biological interest, see Figure 12). The occupancies of the
different clusters show the similarity of the natural product
databases in contrast to the ChEBI database which differs in
chemical space occupation [23]. This findings will be out-
lined in a subsequent publication.
Conclusions
With CDK-Taverna we have presented the first free and
open cheminformatics workflow solution for the biosci-
ences. It allows to link and process data from various
sources in visually accessible workflow diagrams without
any deeper programming experience. Processing of hun-
dreds of thousands of molecules has been demonstrated and
the upper boundary is only limited by the amount of avail-
able memory. The currently implemented workers allow the
processing of chemical data in various formats, provides
the possibility to calculate chemical properties and allows
cluster analysis of molecular descriptor vectors. The use of
the PostgreSQL database with the Pgchem::tigres chemin-
formatics cartridge provides access to chemical databases
with up to a million molecules.
Availability and requirements
• Project name: CDK-Taverna
• Project home page: http://www.cdk-taverna.de
• Operating system(s): Platform independent
• Programming language: Java
• Other requirements: Java 1.6.0 or higher http://
java.sun.com/, Taverna 1.7.2 http://sourceforge.net/
projects/taverna/files/taverna/1.7.2/
• License: GNU Library or Lesser General Public
License (LGPL)
• Any restrictions to use by non-academics: none
Figure 11 Workflow for loading molecular descriptor data vectors from a database, followed by a ART 2-A clustering (Scenario 6).Figure 12 Occupancies of six different detected clusters for two proprietary natural product databases (yellow and red) with the ChEBI da-
tabase (blue), highlighting the unique character of the ChEBI database.

Kuhn et al. BMC Bioinformatics 2010, 11:159
http://www.biomedcentral.com/1471-2105/11/159
Page 11 of 11
Authors' contributions
EW initiated the integration of Taverna and the CDK. CS and AZ conceived the
project, and lead the further development. TK did the majority of CDK-Taverna
development and developed the projects to its current state. All co-authors
contributed to the manuscript. All authors read and approved the final manu-
script.
Acknowledgements
The authors express their gratitude to the Taverna team as well as the CDK
community for creating these great open tools, and like to thank Ernst-Georg
Schmid for his support concerning the Pgchem::tigress functionality.
Author Details
1Institute for Bioinformatics and Cheminformatics, University of Applied
Sciences of Gelsenkirchen, Recklinghausen, Germany, 2Department of
Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden and
3Chemoinformatics and Metabolism, European Bioinformatics Institute (EBI),
Cambridge, UK
References
1. The PubChem Project [http://pubchem.ncbi.nlm.nih.gov/]
2. Irwin J, Shoichet B: ZINC - A Free Database of Commercially Available
Compounds for Virtual Screening. Journal of Chemical Information and
Modeling 2005, 45:177-182.
3. The ChEMBL Group [http://www.ebi.ac.uk/chembl]
4. Williams AJ: Public chemical compound databases. Current opinion in
drug discovery & development 2008, 11(3):393-404.
5. Hassan M, Brown RD, Varma-O'brien S, Rogers D: Cheminformatics
analysis and learning in a data pipelining environment. Molecular
diversity 2006, 10(3):283-299.
6. Shon J, Ohkawa H, Hammer J: Scientific workflows as productivity tools
for drug discovery. Current opinion in drug discovery & development 2008,
11(3):381-388.
7. Pipeline Pilot data analysis and reporting platform [http://
accelrys.com/products/scitegic/]
8. Inforsense Platform [http://www.inforsense.com/products/
core_technology/inforsense_platform/]
9. KNIME Konstanz Information Miner [http://www.knime.org/]
10. Tiwari A, Sekhar AKT: Workflow based framework for life science
informatics. Computational Biology and Chemistry 2007, 31(5-6):305-319.
11. Steinbeck C, Hoppe C, Kuhn S, Guha R, Willighagen EL: Recent
Developments of The Chemistry Development Kit (CDK) - An Open-
Source Java Library for Chemo- and Bioinformatics. Current
Pharmaceutical Design 2006, 12(17):2111-2120.
12. Steinbeck C, Han YQ, Kuhn S, Horlacher O, Luttmann E, Willighagen E: The
Chemistry Development Kit (CDK): An open-source Java library for
chemo- and bioinformatics. Journal of Chemical Information and
Computer Sciences 2003, 43(2):493-500.
13. CDK-Taverna fully recognized [http://chem-bla-ics.blogspot.com/2005/
10/cdk-taverna-fully-recognized.html]
14. CDK-Taverna Release on 2005-10-18 [http://sourceforge.net/projects/
cdk/files/CDK-Taverna/20051018/]
15. Oinn T, Addis M, Ferris J, Marvin D, Senger M, Greenwood M, Carver T,
Glover K, Pocock MR, Wipat A, Li P: Taverna: a tool for the composition
and enactment of bioinformatics workflows. Bioinformatics 2004,
20(17):3045-3054.
16. The Open Source Definition [http://www.opensource.org/docs/osd]
17. Spjuth O, Helmus T, Willighagen EL, Kuhn S, Eklund M, Steinbeck C,
Wikberg JE: Bioclipse: An open rich client workbench for chemo- and
bioinformatics. BMC Bioinformatics 2007, 8(59):.
18. pgchem::tigress: chemoinformatics extension to the PostgreSQL
[http://pgfoundry.org/projects/pgchem/]
19. Apache Maven [http://maven.apache.org/]
20. Taylor IJ, Deelman E, Gannon DB, Shields M: Workflows for e-Science:
23. Kuhn T: Open Source Workflow Engine for Cheminformatics: From Data
Curation to Data Analysis. In PhD thesis University of Cologne; 2009.
24. myExperiment - Tags - Workflows only - cdk-taverna [http://
www.myexperiment.org/tags/914?type=workflows]
25. PostgreSQL Database [http://www.postgresql.org/]
26. GiST Support for PostgreSQL [http://www.sai.msu.su/~megera/
postgres/gist/]
27. Weininger D: SMILES, a chemical language and information system. 1.
Introduction to methodology and encoding rules. Journal of Chemical
Information and Computer Sciences 1988, 28:31-36.
28. Dalby A, Nourse JG, Hounshell WD, Gushurst AKI, Grier DL, Leland BA,
Laufer J: Description of several chemical structure file formats used by
computer programs developed at Molecular Design Limited. Journal of
Chemical Information and Computer Sciences 1992, 32(3):244-255.
29. Murray-Rust P, Rzepa H: Chemical Markup, XML, and the Worldwide
Web. 1. Basic Principles. Journal of Chemical Information and Computer
Sciences 1999, 39(6):928-942.
30. Willighagen EL: Processing CML conventions in Java. Internet Journal of
Chemistry 2001, 4:4+.
31. Kuhn S, Helmus T, Lancashire R, Murray-Rust P, Rzepa H, Steinbeck C,
Willighagen E: Chemical Markup, XML, and the World Wide Web. 7.
CMLSpect, an XML Vocabulary for Spectral Data. Journal of Chemical
Information and Modeling 2007, 47(6):2015-2034.
32. The database of chemical entities of biological Interest (ChEBI) [http://
www.ebi.ac.uk/chebi]
33. Degtyarenko K, de Matos P, Ennis M, Hastings J, Zbinden M, McNaught A,
Alcantara R, Darsow M, Guedj M, Ashburner M: ChEBI: a database and
ontology for chemical entities of biological interest. Nucl Acids Res
2008, 36:344-350.
34. Carpenter G, Grossberg S, Rosen D: ART 2-A: an adaptive resonance
algorithm for rapid categorylearning and recognition. Neural networks
1991, 4(4):493-504.
35. myExperiment - Topological Substructure Workflow [http://
www.myexperiment.org/workflows/557/]
36. myExperiment - Substructure Search on Database Workflow [http://
www.myexperiment.org/workflows/555/]
37. The CDK-Taverna Blog [http://cdktaverna.wordpress.com/2008/09/07/
time-evaluation-for-calculating-molecular-descriptors-using-the-cdk/]
38. myExperiment - Calculation of molecular descriptors for molecules
loaded from database [http://www.myexperiment.org/workflows/563/]
39. myExperiment - Reaction Enumeration Workflow [http://
www.myexperiment.org/workflows/567/]
doi: 10.1186/1471-2105-11-159
Cite this article as: Kuhn et al., CDK-Taverna: an open workflow environment
for cheminformatics BMC Bioinformatics 2010, 11:159
Received: 7 September 2009 Accepted: 29 March 2010
Published: 29 March 2010
This article is available from: http://www.biomedcentral.com/1471-2105/11/159© 2010 Kuhn et l; lic nsee BioMed Central Ltd. is an Open Access article distributed under th terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.BMC Bioinformati s 2010, 11:159Scientific Workflows for Grids. London, Springer; 2007.
21. W3C Web Service Definition Language (WSDL) [http://www.w3.org/
TR/wsdl]
22. W3C SOAP Specifications [http://www.w3.org/TR/soap/]