We have created structure based chemical ontologies that are used to classify chemical compounds automatically. These classifications can be used with success in semantic search engines to find all representatives of a chemical class. In the present paper we would like to demonstrate use cases when utilizing these chemical classes as features in typical machine learning approaches. <br/>Thus, we have used the co-occurrence of chemical compounds with biological and physico-chemical properties in scientific articles to train models that predict properties of novel compounds that did not occur in those training sets. One example is the prediction of hepatotoxicity as well as bioavailability. In principle, one can use any property that is found in the textual vicinity of compounds to build such predictive models. Criteria will be presented that allow to judge the quality and predictive power of such models.

Using chemical ontologies to create molecular prediction systems for any molecular property

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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