ArticleBMC bioinformatics2021
Predicting biological pathways of chemical compounds with a profile-inspired approach.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Probe my Pathway (PmP): a portal to explore the chemical coverage of the human Reactome.Database : the journal of biological databases and curation · 2024Article
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.Briefings in bioinformatics · 2024Review
- Benchmark Dataset for Training Machine Learning Models to Predict the Pathway Involvement of Metabolites.Metabolites · 2023Article
- Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites.bioRxiv : the preprint server for biology · 2023Article
- Sensbio: an online server for biosensor design.BMC bioinformatics · 2023Article
- Predicting protein network topology clusters from chemical structure using deep learning.Journal of cheminformatics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundAssignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites (those in the same biological pathway), based on a fragment-based vectorial representation of their chemical structures.
resultsWe use this representation to predict the biological pathway of a chemical compound with good overall accuracy (AUC 0.74-0.90 depending on the database tested), and analyzed some factors that affect performance. The approach, which is compared with equivalent methods, can in addition detect those molecular fragments characteristic of a pathway.
conclusionsThe method is available as a graphical interactive web server http://csbg.cnb.csic.es/iFragMent .
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Registered trials
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