ArticlePNAS nexus2022
A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.
Article in PNAS nexus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 26 citations in OpenAlex.
- TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.Cell reports methods · 2026Article
- Collateral sensitivity-harnessing microbial vulnerabilities as a solution to antimicrobial resistance.Molecular biology reports · 2026Review
- Advanced technologies of single-cell metabolomics unveiling cellular metabolic heterogeneity for biological and biomedical research.Journal of food and drug analysis · 2026Review
- Targeting metabolism to combat anticancer and antibacterial drug resistance.Trends in pharmacological sciences · 2026Review
- Transcriptome-driven constraint-based modelling reveals metabolic targets for ovarian cancer.Cancer & metabolism · 2026Article
- Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies.bioRxiv : the preprint server for biology · 2026Article
- Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers.Frontiers in pharmacology · 2026Article
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Novel Antimicrobials from Computational Modelling and Drug Repositioning: PotentialMolecules (Basel, Switzerland) · 2025Review
- AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.Exploration of medicine · 2025Article
- Transfer learning predicts species-specific drug interactions in emerging pathogens.bioRxiv : the preprint server for biology · 2024Article
- Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.iScience · 2024Article
- Flux sampling in genome-scale metabolic modeling of microbial communities.BMC bioinformatics · 2024Article
- Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling.Antibiotics (Basel, Switzerland) · 2023Review
- Flux Sampling in Genome-scale Metabolic Modeling of Microbial Communities.bioRxiv : the preprint server for biology · 2023Article
- Editorial: Artificial intelligence for data discovery and reuse in endocrinology and metabolism.Frontiers in endocrinology · 2023Article
- Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis.medRxiv : the preprint server for health sciences · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for
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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.