StereoMapper: clarifying metabolite identity through stereochemically aware relationship assignment
Abstract
Inaccuracies in metabolite cross-mapping frequently introduce errors into genome-scale metabolic models (GEMs). A key challenge lies in distinguishing truly identical metabolites from closely related structural variants, such as stereoisomers, across biochemical databases. StereoMapper addresses this issue by establishing stereochemically aware relationships that clarify metabolite identity. It operates entirely on structural information, using molecular structures to infer equivalence and define relationships. StereoMapper was implemented using RDKit (2025.3.3) and Open Babel-wheel (3.1.1.22) which provides the core functionality of the Open Babel software, and benchmarked across four curated control datasets to evaluate performance in relationship classification (enantiomer, diastereomer, stereo-resolution pairs, and protomer). The pipeline was subsequently applied to 1.3 million molecular structures from multiple metabolic databases to generate equivalent isomeric sets (groups of structures identified as equivalent by StereoMapper). These sets were compared against the human subset of MetaNetX, a context-aware reference, to assess concordance and investigate disagreements. On curated positive control sets containing only positive examples across multiple relationship classes, StereoMapper achieved mean precision, recall, and F1-scores of 93%, 98%, and 95%, respectively; following optimisation, precision and F1 increased to 99% and 98%, while recall remained at 98%. Application to the 1.3 million structures produced over 339,000 stereochemically defined relationships between molecular structures. Comparison with the human subset of MetaNetX assignments showed an overall grouping concordance of 93.5%. The remaining 6.5% of differences were mainly associated with protomer relationships (52%) and stereo-resolution pairs (37%), while 21% involved complex or ambiguous cases.
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BibTeX
@article{mcgoldrick2026stereomapper,
title = {{StereoMapper: clarifying metabolite identity through stereochemically aware relationship assignment}},
author = {Jack McGoldrick and Marco Pagni and Saleh Alwer and Joanne Cooney and Natalia Makosa and Anne Niknejad and Sébastien Moretti and Jadzia Murphy and Filippo Martinelli and Alan Bridge and Ines Thiele and Ronan M. T. Fleming},
journal = {Journal of Cheminformatics},
year = {2026},
month = oct,
doi = {10.1186/s13321-026-01278-3},
url = {https://link.springer.com/10.1186/s13321-026-01278-3},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - StereoMapper: clarifying metabolite identity through stereochemically aware relationship assignment
AU - Jack McGoldrick
AU - Marco Pagni
AU - Saleh Alwer
AU - Joanne Cooney
AU - Natalia Makosa
AU - Anne Niknejad
AU - Sébastien Moretti
AU - Jadzia Murphy
AU - Filippo Martinelli
AU - Alan Bridge
AU - Ines Thiele
AU - Ronan M. T. Fleming
PY - 2026
DA - 2026/10/11
JO - Journal of Cheminformatics
DO - 10.1186/s13321-026-01278-3
UR - https://link.springer.com/10.1186/s13321-026-01278-3
AB - Inaccuracies in metabolite cross-mapping frequently introduce errors into genome-scale metabolic models (GEMs). A key challenge lies in distinguishing truly identical metabolites from closely related structural variants, such as stereoisomers, across biochemical databases. StereoMapper addresses this issue by establishing stereochemically aware relationships that clarify metabolite identity. It operates entirely on structural information, using molecular structures to infer equivalence and define relationships. StereoMapper was implemented using RDKit (2025.3.3) and Open Babel-wheel (3.1.1.22) which provides the core functionality of the Open Babel software, and benchmarked across four curated control datasets to evaluate performance in relationship classification (enantiomer, diastereomer, stereo-resolution pairs, and protomer). The pipeline was subsequently applied to 1.3 million molecular structures from multiple metabolic databases to generate equivalent isomeric sets (groups of structures identified as equivalent by StereoMapper). These sets were compared against the human subset of MetaNetX, a context-aware reference, to assess concordance and investigate disagreements. On curated positive control sets containing only positive examples across multiple relationship classes, StereoMapper achieved mean precision, recall, and F1-scores of 93%, 98%, and 95%, respectively; following optimisation, precision and F1 increased to 99% and 98%, while recall remained at 98%. Application to the 1.3 million structures produced over 339,000 stereochemically defined relationships between molecular structures. Comparison with the human subset of MetaNetX assignments showed an overall grouping concordance of 93.5%. The remaining 6.5% of differences were mainly associated with protomer relationships (52%) and stereo-resolution pairs (37%), while 21% involved complex or ambiguous cases.
SN - 1758-2946
ER -