title: Metabolic pathway predictions for metabolomics: a molecular structure matching approach.
abstract: Metabolic pathways are composed of a series of chemical reactions occurring within a cell. In each pathway, enzymes catalyze the conversion of substrates into structurally similar products. Thus, structural similarity provides a potential means for mapping newly identified biochemical compounds to known metabolic pathways. In this paper, we present TrackSM, a cheminformatics tool designed to associate a chemical compound to a known metabolic pathway based on molecular structure matching techniques. Validation experiments show that TrackSM is capable of associating 93% of tested structures to their correct KEGG pathway class and 88% to their correct individual KEGG pathway. This suggests that TrackSM may be a valuable tool to aid in associating previously unknown small molecules to known biochemical pathways and improve our ability to link metabolomics, proteomic, and genomic data sets. TrackSM is freely available at http://metabolomics.pharm.uconn.edu/?q=Software.html .
authors: ‡Computer Science Department, Helwan University, Cairo, Egypt.
authors: Mai A
authors: MA
authors: Hamdalla
authors: Sanguthevar
authors: S
authors: Rajasekaran
authors: David F
authors: DF
authors: Grant
authors: Ion I
authors: II
authors: Măndoiu
mesh_terms: D000465:Algorithms; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods*; D015394:Molecular Structure; D015203:Reproducibility of Results; D012984:Software