ArticleJournal of chemical information and modeling2015
Metabolic pathway predictions for metabolomics: a molecular structure matching approach.
Article in Journal of chemical information and modeling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- DNA methylation regulates growth traits by influencing metabolic pathways in Pacific white shrimp (Litopenaeus vannamei).BMC genomics · 2025Article
- Learning motif features and topological structure of molecules for metabolic pathway prediction.Journal of cheminformatics · 2025Article
- Review
- A deep learning architecture for metabolic pathway prediction.Bioinformatics (Oxford, England) · 2024Article
- Prediction of plant secondary metabolic pathways using deep transfer learning.BMC bioinformatics · 2023Article
- Predicting biological pathways of chemical compounds with a profile-inspired approach.BMC bioinformatics · 2021Article
- From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.GigaScience · 2017Review
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Authors and funding
4 authors.
Funding
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 .
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Registered trials
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