ReviewFrontiers in pharmacology2022
Machine Learning in Antibacterial Drug Design.
Review in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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
38 citing papers in PubMed, 63 citations in OpenAlex.
- Redefining Therapies for Drug-Resistant Tuberculosis: Synergistic Effects of Antimicrobial Peptides, Nanotechnology, and Computational Design.Advanced healthcare materials · 2026Review
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- Stress-Induced Plant Specialized Metabolism: Signaling, Multi-Omics Integration, and Plant-Derived Antimicrobial Metabolites to Combat Antimicrobial Resistance.Plants (Basel, Switzerland) · 2026Review
- Determination of Potential Inhibitors againstCurrent pharmaceutical design · 2026Review
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Review
- Predicting Antimicrobial Peptide Activity: A Machine Learning-Based Quantitative Structure-Activity Relationship Approach.Pharmaceutics · 2025Article
- Advances and challenges in drug design against dental caries: Application ofJournal of pharmaceutical analysis · 2025Review
- The Next Frontier: Unveiling Novel Approaches for Combating Multidrug-Resistant Bacteria.Pharmaceutical research · 2025Review
- CombattingAntibiotics (Basel, Switzerland) · 2025Review
- Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides.Biomacromolecules · 2025Article
- Artificial intelligence in drug resistance management.3 Biotech · 2025Review
- A Review of In Silico and In Vitro Approaches in the Fight Against Carbapenem-Resistant Enterobacterales.Journal of clinical laboratory analysis · 2025Review
- Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.Journal of chemical information and modeling · 2025Article
- Unlocking the power of antimicrobial peptides: advances in production, optimization, and therapeutics.Frontiers in cellular and infection microbiology · 2025Review
- A subspace learning aided matrix factorization for drug repurposing.BioImpacts : BI · 2025Article
- Translational Horizons in Computer-Aided Drug Discovery: BridgingAnti-inflammatory & anti-allergy agents in medicinal chemistry · 2025Article
- TowardMicroorganisms · 2024Review
- Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.Computational and structural biotechnology journal · 2024Review
- Environmental Antimicrobial Resistance: Implications for Food Safety and Public Health.Antibiotics (Basel, Switzerland) · 2024Review
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.
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.