Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction

Abstract

This work proposes a new structure–activity relationship (SAR) approach to mine molecular fragments that act as structural alerts for biological activity. The entire process is designed to fit with human reasoning, not only to make the predictions more reliable but also to permit clear control by the user in order to meet customized requirements. This approach has been tested on the mutagenicity endpoint, showing marked prediction skills and, more interestingly, bringing to the surface much of the knowledge already collected in the literature as well as new evidence.

Keywords:

• SARpy
• structural alerts
• mutagenicity
• data mining
• SMILES
• QSAR

Acknowledgements

This work was partially supported by the European Commission contract ANTARES and the Italian Ministero della Salute.

Notes

Presented at the 15th International Workshop on Quantitative Structure–Activity Relationships in Environmental and Health Sciences (QSAR2012), 18–22 June 2012, Tallinn, Estonia