ArticlePloS one2011
Predicting biological functions of compounds based on chemical-chemical interactions.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 98 citations in OpenAlex.
- Article
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- Chemical representation standardization needed to generalize metabolic pathway involvement prediction across the Kyoto Encyclopedia of Genes and Genomes, Reactome, and MetaCyc knowledgebases.bioRxiv : the preprint server for biology · 2025Article
- Overlapping and specific bacterial communities in the gut and reproductive system ofFrontiers in microbiology · 2025Article
- Predicting the Pathway Involvement of All Pathway and Associated Compound Entries Defined in the Kyoto Encyclopedia of Genes and Genomes.Metabolites · 2024Article
- Predicting the Association of Metabolites with Both Pathway Categories and Individual Pathways.Metabolites · 2024Article
- Predicting the Pathway Involvement of Metabolites in Both Pathway Categories and Individual Pathways.bioRxiv : the preprint server for biology · 2024Article
- A deep learning architecture for metabolic pathway prediction.Bioinformatics (Oxford, England) · 2024Article
- A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement.PloS one · 2024Article
- Prediction of plant secondary metabolic pathways using deep transfer learning.BMC bioinformatics · 2023Article
- A novel hybrid framework for metabolic pathways prediction based on the graph attention network.BMC bioinformatics · 2022Article
- Predicting protein network topology clusters from chemical structure using deep learning.Journal of cheminformatics · 2022Article
- MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.Bioinformatics (Oxford, England) · 2022Article
- Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects.Computational and mathematical methods in medicine · 2022Article
- Predicting biological pathways of chemical compounds with a profile-inspired approach.BMC bioinformatics · 2021Article
- iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network.Computational and mathematical methods in medicine · 2021Article
- Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy.Computational and mathematical methods in medicine · 2020Article
- The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life.PloS one · 2016Article
- Metabolic pathway predictions for metabolomics: a molecular structure matching approach.Journal of chemical information and modeling · 2015Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.
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