Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification.

ABSTRACT: Chemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification prob

SUBMITTER: Hastings J 

PROVIDER: S-EPMC7962259 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature