ReviewAdvances and applications in bioinformatics and chemistry : AABC2024
Recent Applications of Artificial Intelligence in Discovery of New Antibacterial Agents.
Review in Advances and applications in bioinformatics and chemistry : AABC, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Targeting Bacterial Cell Wall Synthesis: Structural Insights and Emerging Therapeutic Strategies.Pharmaceutics · 2026Review
- Endophytic fungi as a source of antimicrobial compounds against emerging methicillin-resistantMycology · 2026Review
- Review
- Combating Antimicrobial Resistance: Role of Key Stakeholders with Focus on the Pharmaceutical Sector.Pharmaceutical medicine · 2025Review
- Utility of Artificial Intelligence in Antibiotic Development: Accelerating Discovery in the Age of Resistance.Cureus · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) represents today a major challenge for global public health, compromising the effectiveness of treatments against a multitude of bacterial infections. In recent decades, artificial intelligence (AI) has emerged as a promising technology for the identification and development of new antibacterial agents. This review focuses on AI methodologies applied to discover new antibacterial candidates. Case studies that identified small molecules and peptides showing antimicrobial activity and demonstrating efficiency against pathogenic resistant bacteria by employing AI are summarized. We also discuss the challenges and opportunities offered by AI, highlighting the importance of AI progress for the identification of new promising antibacterial drug candidates to combat the AMR.
Indexed as
Identifiers
What Socratic 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 Socratic graph.