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Updated: Sep 12, 2025
14:18
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
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Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification
Roxane Axel Jacob1,2,3, Oliver Wieder1,2, Ya Chen1
1Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, Vienna 1090, Austria.
Journal of Chemical Information and Modeling
|August 6, 2025
Summary
This study introduces aweSOM, a graph neural network model for predicting drug metabolism sites. It quantifies prediction uncertainty, crucial for reliable drug design and development.
Area of Science:
- Computational chemistry
- Pharmacology
- Artificial intelligence in drug discovery
Background:
- In silico metabolism prediction optimizes xenobiotic properties while maintaining biological activity.
- Site-of-metabolism (SOM) models identify metabolically labile atomic positions in drug molecules.
Purpose of the Study:
- To introduce aweSOM, a novel graph neural network (GNN)-based SOM prediction model.
- To leverage deep ensembling for modeling and partitioning predictive uncertainty into aleatoric and epistemic components.
Main Methods:
- Development of a GNN-based model named aweSOM.
- Application of deep ensembling techniques to quantify predictive uncertainty.
- Comprehensive evaluation of uncertainty estimates on a high-quality dataset.
Main Results:
- aweSOM effectively models total predictive accuracy and its uncertainty components.
- Evaluation identified key challenges limiting current SOM prediction model performance.
- The study provides insights into improving metabolism prediction accuracy and reliability.
Conclusions:
- Accurate uncertainty estimation is critical for the practical utility of SOM prediction models.
- Addressing identified challenges can advance the field of in silico metabolism prediction.
- aweSOM offers a robust framework for reliable drug metabolism site identification.
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