Linked open drug data for pharmaceutical research and development

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.

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.

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.