The use of graph matching algorithms to identify biochemical substructures in synthetic chemical compounds: Application to metabolomics

Mai Hamdalla; David Grant; Ion Mandoiu; Dennis Hill; Sanguthevar Rajasekaran; Reda Ammar

doi:10.1109/ICCABS.2012.6182637

The use of graph matching algorithms to identify biochemical substructures in synthetic chemical compounds: Application to metabolomics

IEEE Int Conf Comput Adv Bio Med Sci. 2012 Feb:2012:10.1109/ICCABS.2012.6182637. doi: 10.1109/ICCABS.2012.6182637.

Authors

Mai Hamdalla¹, David Grant², Ion Mandoiu¹, Dennis Hill², Sanguthevar Rajasekaran¹, Reda Ammar¹

Affiliations

¹ Computer Science and Engineering Department, University of Connecticut, Connecticut, USA.
² Pharmaceutical Sciences Department, University of Connecticut, Connecticut, USA.

Abstract

Metabolomics is a rapidly growing field studying the small-molecule metabolite profile of a biological organism. Studying metabolism has a potential to contribute to biomedical research as well as drug discovery. One of the current challenges in metabolomics is the identification of unknown metabolites as existing chemical databases are incomplete. We present a novel way of utilizing known mammalian metabolites in an effort to identify unknown ones. The system relies on a mammalian scaffolds database to aid the classification process. The results show that 96% of the mammalian compounds were identified as truly mammalian in a leave-one-out experiment. The system was also tested with a random set of synthetic compounds, downloaded from ChemBridge and ChemSynthesis databases. The system was able to eliminate 54% of the set, leaving 46% of the compounds as potentially unknown mammalian metabolites.

Keywords: classification; mass spectrometry; metabolites; metabolomics; molecular similarity; structure matching.

Abstract

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