Present chemical data storage methodologies place many restrictions on the use of the stored data. The absence of sufficient high-quality metadata prevents intelligent computer access to the data without human intervention. This creates barriers to the automation of data mining in activities such as quantitative structure−activity relationship modelling. The application of Semantic Web technologies to chemical data is shown to reduce these limitations. The use of unique identifiers and relationships (represented as uniform resource identifiers, URIs, and resource description framework, RDF) held in a triplestore provides for greater detail and flexibility in the sharing and storage of molecular structures and properties.