Mining chemical information from open patents

The enormous increase in the output of scientific data in recent times now requires radical changes in the way in which it is handled. The CAplus database [1] holds more than 32 million references to patents and journal articles and indexes more than 1500 current journals on a weekly basis, while the CAS REGISTRY [2] holds more than 54 million chemical compounds and the CASREACT [3] database more than 39 million single and multi-step reactions. Such resources are created by a labour-intensive process of manual curation with the consequence that a researcher must pay to access them, and the data themselves become a valuable commercial entity. By necessity, this is closed data.The availability of data is vital for data-driven science such as spectra prediction and Quantitative Structure-Activity Relationship (QSAR) modelling, which has become increasingly important to the pharmaceutical industry as it seeks to control the spiralling costs of drug development. Open data - data that is freely available to the community-supports and enables such work. The more the culture of Open data spreads, the more such work becomes viable.This use of Open data for research, though powerful, is not the end of the story. Tim Berners-Lee first described the concept of the Semantic Web [4]. The idea is simple-the World Wide Web comprises a vast collection of information, but information that is largely meaningless to a computer. If it were to be made machine-understandable, then software agents could be developed that would be able use this information as a basis for reasoning and to make decisions. This concept, tied to that of Open data, would allow for computerised scientists conducting their own data-driven research and reporting their conclusions back to humans. The concept of a machine performing research is not one for the world of science fiction-indeed, the robot scientist Adam has conducted its own hypothesis-driven research, reaching conclusions that were later validated by h