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Linked open drug data for pharmaceutical research and development
Journal of Cheminformatics 2011, 3:19 doi:10.1186/1758-2946-3-19
Matthias Samwald (samwald@gmx.at)
Anja Jentzsch (mail@anjajentzsch.de)
Christopher Bouton (chris@entagen.com)
Claus Stie Kallesoe (CSH@lundbeck.com)
Egon Willighagen (egon.willighagen@gmail.com)
Janos Hajagos (janos.hajagos@stonybrook.edu)
M Scott Marshall (marshall@science.uva.nl)
Eric Prud'hommeaux (eric@w3c.org)
Oktie Hassenzadeh (oktie@cs.toronto.edu)
Elgar Pichler (elgar.pichler@gmail.com)
Susie Stephens (susie.stephens@gmail.com)
ISSN 1758-2946
Article type Preliminary communication
Submission date 8 December 2010
Acceptance date 16 May 2011
Publication date 16 May 2011
Article URL http://www.jcheminf.com/content/3/1/19
This peer-reviewed article was published immediately upon acceptance. It can be downloaded,
printed and distributed freely for any purposes (see copyright notice below).
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Journal of Cheminformatics
© 2011 Samwald et al. ; licensee Chemistry 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.

1
Linked open drug data for pharmaceutical research and development

Matthias Samwald1,2,3,*, Anja Jentzsch4, Christopher Bouton5, Claus Stie Kallesøe6, Egon Willighagen7,
Janos Hajagos8, M Scott Marshall9,10, Eric Prud’hommeaux11, Oktie Hassenzadeh12, Elgar Pichler13 and
Susie Stephens14
1Section for Medical Expert and Knowledge-Based Systems, Center for Medical Statistics, Informatics,
and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
2Information Retrieval Facility (IRF), Vienna, Austria.
3Digital Enterprise Research Institute (DERI), National University of Ireland Galway, IDA Business Park,
Lower Dangan, Galway, Ireland.
4Web-based Systems Group, Freie Universität Berlin, Berlin, Germany.
5Entagen, LLC, Second Floor, 44 Merrimac Street, Newburyport, MA 01950, USA.
6H. Lundbeck A/S, Copenhagen, Denmark.
7Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
8Department of Medical Informatics, Stony Brook University School of Medicine, Stony Brook, New York,
USA.
9University of Amsterdam, Amsterdam, The Netherlands.
10Leiden University Medical Center, Leiden, The Netherlands.
11W3C, Cambridge, MA, USA.
12Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
13W3C HCLSIG. W3C, Cambridge, MA, USA.
14Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Radnor, USA.

* Corresponding author
Email
MS: samwald@gmx.at
AJ: mail@anjajentzsch.de
CB: chris@entagen.com
CSK: CSH@lundbeck.com
EW: egon.willighagen@gmail.com
JH: janos.hajagos@stonybrook.edu
MSM: marshall@science.uva.nl
EPH: eric@w3c.org
OH: oktie@cs.toronto.edu
EP: elgar.pichler@gmail.com
SS: susie.stephens@gmail.com

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Abstract
There is an abundance of information about drugs available on the Web. Data sources range from
medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in
clinical trials. These data are typically not connected together, which reduces the ease with which
insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web
Consortium’s (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly
available data about drugs, created Linked Data representations of the data sets, and identified
interesting scientific and business questions that can be answered once the data sets are connected.
The task force provides recommendations for the best practices of exposing data in a Linked Data
representation. In this paper, we present past and ongoing work of LODD and discuss the growing
importance of Linked Data as a foundation for pharmaceutical R&D data sharing.
Findings
Pharmaceutical research has a wealth of available data sources to help elucidate the complex biological
mechanisms that lead to the development of diseases. However, the heterogeneous nature of these
data and their widespread distribution over journal articles, patents and numerous databases makes
searching and pattern discovery a tedious and manual task. From the perspective of a pharmaceutical
research scientist, the ideal data infrastructure should make it easy to link and search across open data
sources in order to identify novel and meaningful correlations and mechanisms. In this paper, we
present work from the Linked Open Drug Data (LODD) task force of the World Wide Web Consortium
(W3C) Health Care and Life Science Interest Group (HCLS IG) that aims to address these issues by
harnessing the power of new web technologies.

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The LODD task force works with a set of technologies and conventions that are now commonly referred
to as Linked Data. The primary goal of the Linked Data movement is to make the World Wide Web not
only useful for sharing and interlinking documents, but also for sharing and interlinking data at very
detailed levels. The movement is driven by the hypothesis that these technologies could revolutionize
global data sharing, integration and analysis, just like the classic Web revolutionized information sharing
and communication over the last two decades.
Linked Data is based on a set of principles and standard recommendations created by the W3C. Single
data points are identified with Hypertext Transfer Protocol (HTTP, [1]) Uniform Resource Identifiers
(URIs). Similar to how a Web page can be retrieved by resolving its HTTP URI (e.g.,
‘http://en.wikipedia.org/wiki/Presenilin’), data about a single entity in the Linked Data space can be
retrieved by resolving its HTTP URI (e.g. ‘http://dbpedia.org/resource/Presenilin’). However, instead of
Web pages, the primary data model of Linked Data is the Resource Description Framework (RDF, [2]). In
RDF, entities, their relations and properties are described with simple subject-predicate-object triples.
Out of these simple triples, sophisticated networks of interlinked data can be built, potentially spanning
over several different locations on the web. Since every entity in this network can be resolved through
HTTP, it is possible to navigate and aggregate the globally distributed data, enabling the important
features of transparency and scalability that made the Web successful.
There is a large array of other standard recommendations based on RDF. Networks of RDF data can be
queried by an intuitive and powerful query language called SPARQL [3]. The Web Ontology Language
(OWL, [4]) makes it possible to do complex logical reasoning and consistency checking of RDF/OWL
resources. These reasoning capabilities can be used to harmonize heterogeneous data structures.
Another related standard is RDFa [5], which makes it possible to embed RDF statements into human-

4
readable Web pages, effectively bridging the domains of human-readable and machine-readable data.
Chen at al. provide an extensive review of RDF/OWL – based projects relevant to drug discovery in a
recent publication [6].
To date, participants of the LODD project have made twelve open-access datasets relevant to
pharmaceutical research and development available as Linked Data (table 1). These are DrugBank [7],
ClinicalTrials.gov [8, 9], DailyMed [10], ChEMBL [11, 12], Diseasome [13], TCMGeneDIT [14, 15], SIDER
[16], STITCH [17], the Medicare formulary and the three most recent additions, RxNorm [18], Unified
Medical Language System (UMLS, [19]) and the WHO Global Health Observatory [20]. To be kept up to
date, the original datasets are periodically retrieved and the Linked Data representations are refreshed.
The URIs for representing entities in the linked datasets are stable and are chosen by the LODD
participants.
Not all of these datasets can currently be considered fully 'open' as outlined by the Panton Principles
[21]. For example, some of the source have non-commercial clauses in the license agreement. The LODD
project is actively exploring the exact conditions for modification and redistribution defined by the data
providers, and acknowledges the limitations with respect to openness some of these datasets currently
have.
The LODD datasets are linked with each other, as well as with datasets provided by other Linked Data
projects, such as Bio2RDF [22] and Chem2Bio2RDF [23], as well as primary data providers that offer
their resources in RDF, such as UniProt [24, 25] and the Allen Brain Atlas [26]. The links between
datasets are depicted in Figure 1. Overall, there are several dozens of biomedically relevant linked
datasets available to date.

5
Table 1: The current LODD datasets. Further information about content and accessibility (URIs, SPARQL
endpoints) of these linked datasets can be found online at [27].
Name Short Description Size and
coverage
(rounded)
Sources Provider (1. original
dataset, 2. RDF version
of dataset)
DrugBank Chemical, pharmacological and
pharmaceutical drug data; data about
drug targets (e.g., sequences, structure,
pathways)
767,000 triples;
4,800 drugs,
2,500 protein
sequences
Aggregated from
various biomedical
and
pharmaceutical
databases
1. University of Alberta
2. Free University of
Berlin
ClinicalTrials.gov /
LinkedCT
Information about clinical trials 9.8 million triples,
80,000 trials
Data submitted by
study sponsors or
their
representatives
1. US National Institute
of Health
2. LinkedCT.org;
University of Toronto
DailyMed Information about approved
prescription drugs, including FDA
approved labels (package inserts)
164,000 triples;
4,000 drugs
Package inserts,
data from the US
food and drug
administration
(FDA)
1. US National Library of
Medicine
2. Free University of
Berlin
ChEMBL Information on drugs, e.g., activity
against drug targets such as proteins,
chemical properties. Linked to primary
literature
24 million triples;
8000 drug
targets, 660,000
compounds
Aggregated from
various biomedical
and
pharmaceutical
databases
1. European
Bioinformatics Institute
2. Uppsala University
Diseasome Characteristics of disorders and disease
genes linked by known disease–gene
associations
91,000 triples;
2,600 genes
Generated from
data in Online
Mendelian
Inheritance in Man
(OMIM)
1. Consortium of several
labs
2. Free University of
Berlin
TCMGeneDIT /
RDF-TCM
Gene-disease-drug associations mined
from literature about Chinese medicine
117,000 triples Mined from
research articles
1. National Taiwan
University
2. Oxford University
RxNorm Prescription drugs, their ingredients,
and national drug codes
7.7 million triples;
166,000 unique
drugs and
ingredients
FDA databases 1. US National Library of
Medicine
2. Stony Brook School of
Medicine
UMLS Unified Medical Language System
(UMLS) sources available without
restrictions
55 million triples Ontologies created
by third parties
1. US National Library of
Medicine
2. Stony Brook School
of Medicine
SIDER Reported adverse effects of marketed 193,000 triples; Mined package 1. European Molecular

6
drugs 63,000 adverse
effect reports
inserts Biology Laboratory,
Heidelberg
2. Free University of
Berlin
STITCH Molecular interactions between
chemicals and proteins
7.5 million
chemicals,
500,000 proteins,
370 organisms
Aggregated from
various biomedical
and
pharmaceutical
databases
1. European Molecular
Biology Laboratory,
Heidelberg
2. Free University of
Berlin
Medicare The Medicare formulary 44,500 triples;
6800 drugs
Primary data 1. US Government
2. Free University of
Berlin
WHO Global
Health
Observatory
Data and statistics for infectious
diseases at country, regional, and global
levels.
354,000 triples Primary data
collected by the
World Health
Organization
1. World Health
Organization
2. Leipzig University

Statistics about size and coverage were last checked on March 24, 2011.

Figure 1: A graph of some of the LODD datasets (dark grey), related biomedical datasets (light grey),
related general-purpose datasets (white) and their interconnections. Line weights correspond to the
number of links. The direction of an arrow indicates the dataset that contains the links, e.g., an arrow
from A to B means that dataset A contains RDF triples that use identifiers from B. Bidirectional arrows
usually indicate that the links are mirrored in both datasets.
While the number of linked biomedical datasets has grown significantly over the last years, there is still
a marked lack of mature applications that enable end-users to explore and query these datasets. Linked
data browsers such as Marbles [28] or Sig.ma [29, 30] are currently too generic for most end-users
(although they can be very helpful for developers). These shortcomings are addressed by TripleMap
(Figure 2, [31]), a new web-based application that can be used for the navigation, visualization and
analysis of the LODD resources and other RDF datasets. To illustrate the use of TripleMap and the LODD
resources, the following simple scenario could be imagined: A researcher interested in Alzheimer’s

7
Disease decides to find out everything that they can about the disease by querying an integrated version
of the Linking Open Drug Data (LODD) sets. They open TripleMap and start their search by typing
“Alzheimer’s” into the Diseases search box. As they type, TripleMap provides a dynamic auto-complete
list of all of the disease related entities across all LODD data sets that match the search string. The
researcher selects “Alzheimer’s Disease” and drags and drops it into the TripleMap workspace. Now, the
researcher can view a range of information known about the properties of the disease in the right-hand
“properties panel” including links out to Pubmed, Online Mendelian Inheritance in Man (OMIM, [32]),
Uniprot [24] and other sources. These sources provide the user with rapid access to overview
information about the disease.
Figure 2: TripleMap (www.triplemap.com) is a web-based application that provides a rich, dynamic,
visual interface to integrated RDF datasets such as the LODD. On the left hand side of the application a
researcher uses an icon-based menu representing biomedical entities such as compounds, diseases and
assays to search for entities and view their associations. Entities can be dragged and dropped from the
icon menu into the application’s zoomable workspace. In the middle of the application the user navigates
maps of entities and their associations in the zoomable workspace much like users of Google Maps are
able to scan and zoom into and out of geographically based maps. On the right hand side of the
application the user can view an integrated set of all of the available properties for a selected entity. As
entities are added to the workspace the system automatically generates semantically tagged edges
between associated entities.
The researcher is now interested in discovering entities that are associated with Alzheimer’s Disease.
They select the Alzheimer’s Disease icon in the workspace and the system automatically shows them a
number of associated disease genes provided by Diseasome, compounds provided by DrugBank and

8
DailyMed, and clinical trials provided by LinkedCT. The researcher starts to explore relationships
between entities by selecting two genes, presenilin (PSEN1) and amyloid precursor protein (APP), and
dragging them into the workspace. In addition to finding genes related to Alzheimer’s Disease, the user
is interested in compounds known to be related to the disease. The user finds several compounds and
pulls them into the workspace. The user is also interested in finding out what clinical trials are currently
being run for Alzheimer’s Disease and the system shows 200 such trials. With a simple click and drag
action they pull all 200 trials into the workspace. As entities are added to the workspace, if there are
known associations between them, those associations are also shown to the user as semantically tagged
edges. This ability to show a researcher unexpected associations between entities that are related to
their field of interest is at the heart of the value of an application like TripleMap and the extensive, rich,
interconnected data available in the LODD data sets.
Linked Data as an emerging technology is still not free from shortcomings. A major problem is the
heterogeneity in how data is modeled. Even when the entities between datasets are mapped to each
other, it can still be difficult to intuitively write queries that span datasets because of this heterogeneity.
This problem is being addressed by another task force of the W3C HCLSIG, which aims to bridge the data
in the growing number of LODD datasets with a well-engineered top-level ontology, the translational
medicine ontology (TMO, [33]). Another problem is how to efficiently query RDF in distributed SPARQL
databases without requiring the aggregation of RDF data at a central location. Again, this is addressed by
ongoing work on query federation by members of the W3C HCLS IG [34]. Finally, there has been a lack of
applications with good user interfaces to make Linked Data resources accessible to end-users outside
the biomedical informatics community. This is addressed by several ongoing endeavors such as the
European Khresmoi project [35].

9
A challenge to creating linked data that is specific to the domain of chemistry is the provision of
chemical identifiers. It is for this reason that W3C HCLS IG supports efforts to standardize unique
identifiers for chemical compounds such as the IUPAC International Chemical Identifier (InChI, [36]).
The pharmaceutical industry is starting to embrace Linked Data with examples of projects being
presented by Eli Lilly, Johnson & Johnson and UCB Pharma. While the adoption of Linked Data is still not
yet very widespread in individual companies, it is on the agenda of several large-scale cross-pharma
projects. An European project, the Open Pharmacological Space (OPS) Open PHACTS (Pharmacological
Concept Triple Store) project under the European Innovative Medicines Initiative (IMI, [37]) wants to
create an open source, open standards and open access infrastructure to enable integration of chemical
and biological data to support drug discovery. The project intends to reach this goal by using Linked Data
and managing the data in an RDF triple store. Collaboration across several IMI projects should also
encourage the coordinated use of Linked Data to enhance data sharing. On the pre-competitive data
sharing side of pharmaceutical informatics, the members of the Pistoia Alliance [38] are developing the
Semantically Enriched Scientific Literature (SESL) project. The goal of SESL is to test the feasibility of
executing federated querying across full text literature and bioinformatics databases by performing
SPARQL queries on a triple store of assertions from the chosen data sources. The PRISM Forum [39] has
also issued a letter recommending the adoption of Linked Data that has been supported by its
membership of 15 of the top 20 pharmaceutical companies. The European OpenTox project [40, 41]
uses RDF as a standard for the exchange of predictive toxicology related data. The OpenTox framework
defines algorithms, models, data sets, and chemical compounds, in a distributed data storage and
computing facility.

10
Proprietary systems for providing integrated pharmaceutical data exist. The Accelrys / Symyx products
[42] are popular examples, and can be both accessed online or installed locally. Accessing the data
provided by these products often requires proprietary tools and internal installations also require
ongoing work to be kept up-to-date. Furthermore, many of these products are based on individual
databases that are not linked. Since the amount of data and the number of potential data sources is
growing, it will become harder for single software vendors to create all-encompassing solutions. The
nascent Linked Data infrastructure could help to make the creation of integrated solution more
sustainable, easier to maintain and vendor-neutral.
Over the next years, the LODD group will continue to work jointly with both academic and industry
partners. It will aim to become an umbrella for other Linked Data providers and consumers in the
pharmaceutical domain, assisting with documentation, interlinking, quality management, and
compliance with standard formats and vocabularies. Another strand of work will focus on how to
integrate public Linked Data with non-public, in-house datasets of biomedical research institutions and
pharmaceutical companies.
The LODD task force is open to new participants and interested individuals or groups are invited to get in
contact with the authors of this paper.
Competing interests
CB declares association with Entagen, LLC, a for-profit company that is building commercial software for
semantic technologies such as TripleMap. All other authors declare no competing interests.

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Authors’ contributions
MS wrote major parts of the manuscript and organized the paper writing process. AJ converted several
of the LODD datasets. CB developed the TripleMap software. SS organized the Linked Open Drug Data
task force. All authors participated in discussions and developments of the Linked Open Drug Data task
force of the W3C Health Care and Life Science Interest Group. All authors read and approved the final
manuscript.
Acknowledgements
We acknowledge the article processing charge for this article that has been partially funded by Pfizer,
Inc. Pfizer, Inc. has had no input into the content of the article. The article has been independently
prepared by the authors and been subject to the journal's standard peer review process.
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TCM
Gene
DIT
Linked
CT
Geo
Names
STITCH
SIDER
Medi
Care
Drug
Bank
Disea-
some
Daily
Med
DBpedia
UniProt
PubMed
Pub
Chem
Pfam
PDB
OMIM
KEGG
Cpd
HGNC
GeneID
ChEBI
CAS
RxNorm
UMLS
WHO
GHO
Graphical abstract

TCM
Gene
DIT
Linked
CT
Geo
Names
STITCH
SIDER
Medi
Care
Drug
Bank
Disea-
some
Daily
Med
DBpedia
UniProt
PubMed
Pub
Chem
Pfam
PDB
OMIM
KEGG
Cpd
HGNC
GeneID
ChEBI
CAS
RxNorm
UMLS
WHO
GHO
Figure 1

Figure 2