ArticleJournal of chemical information and modeling2025
Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- FAME3R: an efficient, practical and reliable open-source tool for predicting phase 1 and phase 2 sites of metabolism.Journal of cheminformatics · 2026Article
- Automated Annotation of Sites of Metabolism from Biotransformation Data.Journal of chemical information and modeling · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In silico metabolism prediction models have become indispensable tools to optimize the metabolic properties of xenobiotics while preserving their intended biological activity. Among these, site-of-metabolism (SOM) prediction models are particularly valuable for pinpointing metabolically labile atomic positions. However, the practical utility of these models depends not only on their ability to deliver accurate predictions but also on their capacity to provide reliable estimates of predictive uncertainty. In this work, we introduce aweSOM, a graph neural network (GNN)-based SOM prediction model that leverages deep ensembling to model the total predictive accuracy and partition it into its aleatoric and epistemic components. We conduct a comprehensive evaluation of aweSOM's uncertainty estimates on a high-quality data set, identifying key challenges that currently constrain the performance of SOM prediction models. Based on these findings, we propose actionable insights to drive progress in the field of metabolism prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.