ReviewAntibiotics (Basel, Switzerland)2022
Deep Learning and Antibiotic Resistance.
Review in Antibiotics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Assessment of the rat acute oral toxicity of quinoline-based pharmaceutical scaffold molecules using QSTR, q-RASTR and machine learning methods.Molecular diversity · 2026Article
- Automatic detection and quantification of antimicrobial inhibition zones using YOLO11n withFrontiers in microbiology · 2026Article
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Future perspectives in esophageal manometry.World journal of gastrointestinal surgery · 2025Review
- Artificial intelligence in drug resistance management.3 Biotech · 2025Review
- Translational Horizons in Computer-Aided Drug Discovery: BridgingAnti-inflammatory & anti-allergy agents in medicinal chemistry · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Androgen receptor dynamics in prostate cancer: from disease progression to treatment resistance.Frontiers in oncology · 2025Review
- AMRColab - a user-friendly antimicrobial resistance detection and visualization tool.Microbial genomics · 2024Article
- A Comprehensive Review of Silver and Gold Nanoparticles as Effective Antibacterial Agents.Pharmaceuticals (Basel, Switzerland) · 2024Review
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Review
- The Silent Threat: Antimicrobial-Resistant Pathogens in Food-Producing Animals and Their Impact on Public Health.Microorganisms · 2023Review
- Improving the Detection of Epidemic Clones inMicroorganisms · 2023Article
- Personal Care Products as a Contributing Factor to Antimicrobial Resistance: Current State and Novel Approach to Investigation.Antibiotics (Basel, Switzerland) · 2023Review
- Antimicrobial resistance and machine learning: past, present, and future.Frontiers in microbiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.
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.