The ChEMBL database as linked open data

Egon L Willighagen,Antony J Williams,David J Wild,Peter Ansell,Valery Tkachenko,Andra Waagmeester,Bin Chen,Janna Hastings,Ola Spjuth

doi:10.1186/1758-2946-5-23

Abstract

BackgroundMaking data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources. RDF documents can natively link to related data, and others can link back using Uniform Resource Identifiers (URIs). RDF makes the data machine-readable and uses extensible vocabularies for additional information, making it easier to scale up inference and data analysis.ResultsThis paper describes recent developments in an ongoing project converting data from the ChEMBL database into RDF triples. Relative to earlier versions, this updated version of ChEMBL-RDF uses recently introduced ontologies, including CHEMINF and CiTO; exposes more information from the database; and is now available as dereferencable, linked data. To demonstrate these new features, we present novel use cases showing further integration with other web resources, including Bio2RDF, Chem2Bio2RDF, and ChemSpider, and showing the use of standard ontologies for querying.ConclusionsWe have illustrated the advantages of using open standards and ontologies to link the ChEMBL database to other databases. Using those links and the knowledge encoded in standards and ontologies, the ChEMBL-RDF resource creates a foundation for integrated semantic web cheminformatics applications, such as the presented decision support.

Highlights

Making data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources
This paper presents an update on the ChEMBL-RDF data set, including details of the latest structures and ontologies used to map ChEMBL to RDF, along with new use cases, showing links to other datasets using their RDF linked data Uniform Resource Identifiers (URIs) to further support research in the life sciences
Because the primary purpose of this paper is to expose the ChEMBL data as linked data, less focus has been placed on ontologically capturing the fine details of the pharmacological literature

Summary

Introduction

Making data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources. Scientists wish to discover new, unique, and significant patterns in datasets that explain biological phenomena not yet understood. Systems biologists integrate micro-array differential expression datasets to biological pathways, using various other datasets to provide evidence for the links [1]. Another prominent example is drug discovery, in which a new unique chemical entity is designed or discovered based on a description of its required biological properties. This process requires the effective linkage of many

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