ArticleGut microbes2026
High-throughput identification of bacterial β-glucuronidase inhibitors using machine learning.
Article in Gut microbes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human gut microbiome plays a vital role in regulating host physiological functions and influencing the pharmacokinetics of interventions, particularly drug metabolism, which in turn affects pharmacodynamics. Gut microbial β-glucuronidase (GUS) is a key bacterial enzyme that modulates drug therapeutic outcome and gastrointestinal toxicity through deconjugating glucuronidated drug metabolites. Despite this, systematic high-throughput prediction of GUS inhibitors remains limited by sparse experimental data and the translational shortcomings of known compounds. Here, machine learning is applied as a powerful tool to identify potential GUS inhibitors from more than 10,000 FDA-approved drugs, food additives, and excipients. In this study, both unsupervised and supervised machine learning models were trained on literature-derived data describing the inhibitory potency of 122 compounds against
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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.