ArticlePharmaceutics2021
Machine Learning Uncovers Adverse Drug Effects on Intestinal Bacteria.
Article in Pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
12 citing papers in PubMed.
- Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning.ACS pharmacology & translational science · 2026Article
- Industrial and agricultural chemicals exhibit antimicrobial activity against human gut bacteria in vitro.Nature microbiology · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Natural Compounds for Bone Remodeling: A Computational and Experimental Approach Targeting Bone Metabolism-Related Proteins.International journal of molecular sciences · 2024Article
- Human Gut Microbiota in Heart Failure: Trying to Unmask an Emerging Organ.Biomedicines · 2023Review
- Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features.BMC bioinformatics · 2023Article
- The Interplay between Gut Microbiota and Oral Medications and Its Impact on Advancing Precision Medicine.Metabolites · 2023Review
- A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.International journal of environmental research and public health · 2022Review
- Machine Learning and Artificial Intelligence in Toxicological Sciences.Toxicological sciences : an official journal of the Society of Toxicology · 2022Review
- Network metrics, structural dynamics and density functional theory calculations identified a novel Ursodeoxycholic Acid derivative against therapeutic target Parkin for Parkinson's disease.Computational and structural biotechnology journal · 2022Article
- Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development.Pharmaceutics · 2021Article
- Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota.Pharmaceutics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The human gut microbiome, composed of trillions of microorganisms, plays an essential role in human health. Many factors shape gut microbiome composition over the life span, including changes to diet, lifestyle, and medication use. Though not routinely tested during drug development, drugs can exert profound effects on the gut microbiome, potentially altering its functions and promoting disease. This study develops a machine learning (ML) model to predict whether drugs will impair the growth of 40 gut bacterial strains. Trained on over 18,600 drug-bacteria interactions, 13 distinct ML models are built and compared, including tree-based, ensemble, and artificial neural network techniques. Following hyperparameter tuning and multi-metric evaluation, a lead ML model is selected: a tuned extra trees algorithm with performances of AUROC: 0.857 (±0.014), recall: 0.587 (±0.063), precision: 0.800 (±0.053), and f1: 0.666 (±0.042). This model can be used by the pharmaceutical industry during drug development and could even be adapted for use in clinical settings.
Indexed as
Identifiers
What OpenQuestion holds
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.