ArticleMolecular systems biology2016
Chemogenomics and orthology-based design of antibiotic combination therapies.
Article in Molecular systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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.
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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
66 citing papers in PubMed, 125 citations in OpenAlex.
- Bioactivity-driven prediction of antibacterial synergy using machine learning models.PLoS computational biology · 2026Article
- TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.Cell reports methods · 2026Article
- Teaching artificial intelligence through drug-drug interaction clustering analysis: Integrating project-based learning and large language models.PLoS computational biology · 2026Article
- A comprehensive review of cluster methods for drug-drug interaction network.Quantitative biology (Beijing, China) · 2026Review
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Orthology and near-cographs in the context of phylogenetic networks.Algorithms for molecular biology : AMB · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- Artificial intelligence in drug resistance management.3 Biotech · 2025Review
- Systematic mapping of antibiotic cross-resistance and collateral sensitivity with chemical genetics.Nature microbiology · 2025Article
- AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.Exploration of medicine · 2025Article
- GEODE: anFrontiers in pharmacology · 2025Article
- The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution.Evolutionary applications · 2024Article
- Analysis of Co-localized Biosynthetic Gene Clusters Identifies a Membrane-Permeabilizing Natural Product.Journal of natural products · 2024Article
- Transfer learning predicts species-specific drug interactions in emerging pathogens.bioRxiv : the preprint server for biology · 2024Article
- Measuring drug similarity using drug-drug interactions.Quantitative biology (Beijing, China) · 2024Article
- Automated and miniaturized screening of antibiotic combinationsActa pharmaceutica Sinica. B · 2024Article
- Critical role of growth medium for detecting drug interactions in Gram-negative bacteria that modelmBio · 2024Article
- CAD204520 TargetsInternational journal of molecular sciences · 2024Article
- Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling.Cancers · 2023Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
Abstract
Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical-genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug-interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less-studied pathogens by leveraging chemogenomics data in model organisms.
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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.