ArticleApplied microbiology and biotechnology2021
Simultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learning.
Article in Applied microbiology and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Artificial Intelligence in Selected Domains of Drug Discovery: A Critical Narrative Review.Drug design, development and therapy · 2026Review
- Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting.Metabolites · 2024Article
- Sensing of Antibiotic-Bacteria Interactions.Antibiotics (Basel, Switzerland) · 2023Review
- The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy.ACS omega · 2023Article
- High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery.Metabolites · 2023Review
- Epigenetic and Drug Response Modulation of Epigalocaten-In-3-Gallate inAntibiotics (Basel, Switzerland) · 2023Article
- State of the Art on Developments of (Bio)Sensors and Analytical Methods for Rifamycin Antibiotics Determination.Sensors (Basel, Switzerland) · 2023Review
- Prediction of Potential Natural Antibiotics Plants Based on Jamu Formula Using Random Forest Classifier.Antibiotics (Basel, Switzerland) · 2022Article
- A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.PNAS nexus · 2022Article
- Article
- β-Lactam potentiators to re-sensitize resistant pathogens: Discovery, development, clinical use and the way forward.Frontiers in microbiology · 2022Review
- Artificial Intelligence and Antibiotic Discovery.Antibiotics (Basel, Switzerland) · 2021Review
- Technologies for High-Throughput Identification of Antibiotic Mechanism of Action.Antibiotics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The low rate of discovery and rapid spread of resistant pathogens have made antibiotic discovery a worldwide priority. In cell-based screening, the mechanism of action (MOA) is identified after antimicrobial activity. This increases rediscovery, impairs low potency candidate detection, and does not guide lead optimization. In this study, high-throughput Fourier-transform infrared (FTIR) spectroscopy was used to discriminate the MOA of 14 antibiotics at pathway, class, and individual antibiotic level. For that, the optimal combinations and parametrizations of spectral preprocessing were selected with cross-validated partial least squares discriminant analysis, to which various machine learning algorithms were applied. This coherently resulted in very good accuracies, independently of the algorithms, and at all levels of MOA. Particularly, an ensemble of subspace discriminants predicted the known pathway (98.6%), antibiotic classes (100%), and individual antibiotics (97.8%) with exceptional accuracy, and similar results were obtained for simulated novel MOA. Even at very low concentrations (1 μg/mL) and growth inhibition (15%), over 70% pathway and class accuracy was achieved, suggesting FTIR spectroscopy can probe the grey chemical matter. Prediction of inhibitory effect was also examined, for which a squared exponential Gaussian process regression yielded a root mean square error of 0.33 and a R
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