ArticleMolecular pharmaceutics2025
Metabolite Identification Data in Drug Discovery, Part 2: Site-of-Metabolism Annotation, Analysis, and Exploration for Machine Learning.
Article in Molecular pharmaceutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Computational Identification of Active Drug Metabolites for Human Protein Targets.Molecular pharmaceutics · 2026Article
- ATTNSOM: learning cross-isoform attention for cytochrome P450 site-of-metabolism prediction.Bioinformatics (Oxford, England) · 2026Article
- Stereoselective Biotransformation: Transfer of Learning to Advance Drug Metabolism and Biocatalysis.Angewandte Chemie (International ed. in English) · 2026Review
- Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis.Molecular pharmaceutics · 2025Article
- Metabolite Identification Data in Drug Discovery, Part 2: Site-of-Metabolism Annotation, Analysis, and Exploration for Machine Learning.Molecular pharmaceutics · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to pinpoint and predict sites of metabolism (SoMs) is essential for designing and optimizing effective and safe bioactive small molecules. However, the number of molecules with annotated SoMs is limited, hindering the advancement of data-driven methods such as machine learning for metabolism prediction. Here, we provide a comprehensive characterization of SoM data obtained from the readouts of a human hepatocyte assay conducted at AstraZeneca Gothenburg. We explore a new strategy for SoM annotation that accounts for uncertainty in the experimental data, and we relate our findings to the most comprehensive SoM data collection available to date. Our study includes entropy analysis of SoM annotations, accompanied by representative examples that highlight the complexities of interpreting and working with metabolism data. Furthermore, we demonstrate the impact and value of the new metabolism data on SoM prediction. Importantly, a substantial portion of the data generated and analyzed as part of this work is made publicly available.
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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 Socratic graph.