Chemical data representation is a challenge that has been addressed using different methodologies. Representation includes not only a set of unique chemical descriptors for the molecules themselves, but also the linking process (reactions) that they belong to in the form of metadata. The structured nature of this data makes it easy to store in structured databases. However, one common issue remains: the low quality of metadata associated with each chemical entity. This could hinder the extraction of meaningful knowledge from the stored information without time consuming human intervention. Efforts have been made in a) the optimization of chemical and reaction representation in order to achieve real-time text and data mining and b) the integration of chemical information with semantic analysis of surrounding text generated by researchers. In this talk we will focus on addressing the first issue in detail and discuss strategies for the second part.<br/>We will provide the background on chemical /reactions representations used by Dotmatics and the tools that enable Chemists to generate these into a comprehensive chemistry toolkit. Additionally this talk will cover how chemistry descriptors can be converted into computer fingerprints or bit-strings, allowing high performance searching (super and sub-structure searching) and ranking of chemistry data. These solutions also take advantage of advanced memory mapping and threading to provide interactive capability additional to those available on standard laptop computers. This enables data discovery to done at the application level instantly and without recourse to large scale server infrastructure.<br/>Finally, we will explore how all Dotmatics technologies can make use of standardize ontology dictionaries and other commercially available natural language processing (NLP) based text mining tools providing additional added value in the knowledge discovery process.

Strategies for creating knowledge from chemistry and text data

251st National Meeting (2016)

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