ArticleCell2019
A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.
Article in Cell, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 175 papers, 2 of them syntheses that pooled it.
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
175 citing papers in PubMed, 2 syntheses or guidelines pooled it, 337 citations in OpenAlex.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- Chloramphenicol stress triggers oxidative adaptation inmSystems · 2026Article
- Aminotetrazole-Gold Nanocomplexes Shunt Electrons to Eradicate Bacteria Without ROS-Induced Resistance.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Translation Inhibiting Antibiotics Induce an asRNA Regulating Purine Metabolism inbioRxiv : the preprint server for biology · 2026Article
- Selecting methods for draft GEM generation in multicellular eukaryotes: a comparative analysis.BMC bioinformatics · 2026Article
- Revealing transcriptomic responses inmSystems · 2026Article
- Metabolic reconstruction reveals ATP salvage as a key response to trimethoprim treatment.iScience · 2026Article
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- Article
- The toxin from the PemIK-Sa1 toxin-antitoxin system decreasesFrontiers in microbiology · 2026Article
- Fermentative growth decreases the iron demand of Staphylococcus aureus.Journal of inorganic biochemistry · 2026Article
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Accurate prediction of gene deletion phenotypes with Flux Cone Learning.Nature communications · 2025Article
- Uridine as a potentiator of aminoglycosides through activation of carbohydrate transporters.Science advances · 2025Article
- Staphylococcus aureus COL: An Atypical Model Strain of MRSA That Exhibits Slow Growth and Antibiotic Tolerance due to a Mutation in PRPP Synthetase.Molecular microbiology · 2025Article
- The impact of bacterial purine metabolism on antibiotic efficacy.npj antimicrobials and resistance · 2025Review
- Implications From the Analogous Relationship Between Evolutionary and Learning Processes.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
115 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
Abstract
Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.
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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.