Literature DB >> 20405959

A semantic web ontology for small molecules and their biological targets.

Jooyoung Choi1, Melissa J Davis, Andrew F Newman, Mark A Ragan.   

Abstract

A wide range of data on sequences, structures, pathways, and networks of genes and gene products is available for hypothesis testing and discovery in biological and biomedical research. However, data describing the physical, chemical, and biological properties of small molecules have not been well-integrated with these resources. Semantically rich representations of chemical data, combined with Semantic Web technologies, have the potential to enable the integration of small molecule and biomolecular data resources, expanding the scope and power of biomedical and pharmacological research. We employed the Semantic Web technologies Resource Description Framework (RDF) and Web Ontology Language (OWL) to generate a Small Molecule Ontology (SMO) that represents concepts and provides unique identifiers for biologically relevant properties of small molecules and their interactions with biomolecules, such as proteins. We instanced SMO using data from three public data sources, i.e., DrugBank, PubChem and UniProt, and converted to RDF triples. Evaluation of SMO by use of predetermined competency questions implemented as SPARQL queries demonstrated that data from chemical and biomolecular data sources were effectively represented and that useful knowledge can be extracted. These results illustrate the potential of Semantic Web technologies in chemical, biological, and pharmacological research and in drug discovery.

Mesh:

Year:  2010        PMID: 20405959     DOI: 10.1021/ci900461j

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  5 in total

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Journal:  Bioinformatics       Date:  2011-05-05       Impact factor: 6.937

2.  Many InChIs and quite some feat.

Authors:  Wendy A Warr
Journal:  J Comput Aided Mol Des       Date:  2015-06-17       Impact factor: 3.686

3.  The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

Authors:  Santiago Vilar; George Hripcsak
Journal:  Brief Bioinform       Date:  2017-07-01       Impact factor: 11.622

4.  Improving integrative searching of systems chemical biology data using semantic annotation.

Authors:  Bin Chen; Ying Ding; David J Wild
Journal:  J Cheminform       Date:  2012-03-08       Impact factor: 5.514

5.  Cheminformatics and the Semantic Web: adding value with linked data and enhanced provenance.

Authors:  Jeremy G Frey; Colin L Bird
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2013-01-08
  5 in total

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