Biosynthetic Plausibility Mapper: Reaction-Aware Prioritization of Plant Metabolite Candidates Using Curated Pathway Context
- 1Instituto de Pesquisas de Produtos Naturais Walter Mors
- 2Universidade Federal do Rio de Janeiro
Abstract
Untargeted plant metabolomics can generate numerous structurally plausible annotations and candidate structures, but structural similarity alone does not establish whether a compound is consistent with the biosynthetic context represented by the detected metabolome. Integrating curated biochemical relationships with chemical similarity could therefore provide an additional, interpretable layer for candidate prioritization. Here, we present Biosynthetic Plausibility Mapper (BPM), an opensource cheminformatics application that maps user-provided plant metabolite annotations onto a locally constructed PlantCyc-derived knowledge base and explicitly distinguishes detected compounds, curated pathway context, and computationally prioritized candidates. BPM integrates chemical-identity reconciliation, curated reaction and pathway relationships, structural similarity, and global-context coherence into a decomposable candidate-ranking framework. In pathway-based benchmarking against hard structural decoys, BPM v3.1 achieved AUROC and AUPRC values of 1.000 and ranked the reference compound first in all 20 trials. In a more stringent adversarial benchmark containing biosynthetically plausible alternatives, v3.1 improved AUROC from 0.920 to 0.985, AUPRC from 0.574 Biosynthetic Plausibility Mapper to 0.837, and Top-1 accuracy from 65% to 90% relative to v3. A compact phenylpropanoid–flavonoid example illustrates the separation of experimental annotations, curated context, and prioritized hypotheses, while a larger Arabidopsis occurrence dataset demonstrates application to 864 mapped compounds within a 6,714-node network. BPM therefore provides an interpretable framework for converting annotated plant metabolite lists into ranked biosynthetic hypotheses without conflating computational plausibility with compound identification. By combining curated biochemical context with structural evidence and exporting prioritized candidates for targeted interrogation of LC–MS/MS data, BPM provides a practical link between metabolite annotation, biosynthetic interpretation, and subsequent experimental investigation.
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BibTeX
@misc{patricio2026biosynthetic,
title = {{Biosynthetic Plausibility Mapper: Reaction-Aware Prioritization of Plant Metabolite Candidates Using Curated Pathway Context}},
author = {Lucas Jesus Patrício and Rômulo Pereira de Jesus and Ísis Tavares Vilas Boas and Fernanda Oliveira Chagas and Ricardo Moreira Borges},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009098/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009098/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Biosynthetic Plausibility Mapper: Reaction-Aware Prioritization of Plant Metabolite Candidates Using Curated Pathway Context
AU - Lucas Jesus Patrício
AU - Rômulo Pereira de Jesus
AU - Ísis Tavares Vilas Boas
AU - Fernanda Oliveira Chagas
AU - Ricardo Moreira Borges
PY - 2026
DA - 2026/09/20
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009098/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009098/v1
AB - Untargeted plant metabolomics can generate numerous structurally plausible annotations and candidate structures, but structural similarity alone does not establish whether a compound is consistent with the biosynthetic context represented by the detected metabolome. Integrating curated biochemical relationships with chemical similarity could therefore provide an additional, interpretable layer for candidate prioritization. Here, we present Biosynthetic Plausibility Mapper (BPM), an opensource cheminformatics application that maps user-provided plant metabolite annotations onto a locally constructed PlantCyc-derived knowledge base and explicitly distinguishes detected compounds, curated pathway context, and computationally prioritized candidates. BPM integrates chemical-identity reconciliation, curated reaction and pathway relationships, structural similarity, and global-context coherence into a decomposable candidate-ranking framework. In pathway-based benchmarking against hard structural decoys, BPM v3.1 achieved AUROC and AUPRC values of 1.000 and ranked the reference compound first in all 20 trials. In a more stringent adversarial benchmark containing biosynthetically plausible alternatives, v3.1 improved AUROC from 0.920 to 0.985, AUPRC from 0.574 Biosynthetic Plausibility Mapper to 0.837, and Top-1 accuracy from 65% to 90% relative to v3. A compact phenylpropanoid–flavonoid example illustrates the separation of experimental annotations, curated context, and prioritized hypotheses, while a larger Arabidopsis occurrence dataset demonstrates application to 864 mapped compounds within a 6,714-node network. BPM therefore provides an interpretable framework for converting annotated plant metabolite lists into ranked biosynthetic hypotheses without conflating computational plausibility with compound identification. By combining curated biochemical context with structural evidence and exporting prioritized candidates for targeted interrogation of LC–MS/MS data, BPM provides a practical link between metabolite annotation, biosynthetic interpretation, and subsequent experimental investigation.
ER -