Centuries of discoveries in chemistry and biology have produced a large amount of knowledge about chemicals and their interactions with the environment. Recent efforts to harvest and store this data into electronic warehouses have emphasized two issues: 1) the scarcity and incompleteness of the data, and 2) the need to organize it into more comprehensible and exchangeable formats. Moreover, organizing chemical information in a structured way could not only improve our understanding of chemistry and related sciences, but also facilitate new discoveries. In this spirit, we have developed ClassyFire and ChemOnt. ClassyFire is a computational tool for a rapid, consistent, dataset-independent, automated structure-based classification of chemical compounds. It relies on the structure-based sub-ontology of ChemOnt, a well-defined chemical ontology, which covers a wide spectrum of compounds, and roles (applications, health effects, etc.). In a join effort, ChemOnt has been mapped to other ontologies for the sake of interoperability. ClassyFire was used to classify major databases, including PubChem, DrugBank, HMDB, and ChEBI. Additionally, we have developed other tools and frameworks to integrate more concepts (proteins, pathways, phenotypes) in order to represent or study their interactions with small molecules. In this presentation, we will describe these tools, some of their applications, and how they could be combined with semantic technologies to infer knowledge, suggest new hypothesis, as well as make new discoveries.

Automated structural and functional annotation of small molecules using integrated chemical ontologies: ClassyFire, ChemOnt, and downstream applications

251st National Meeting (2016)

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