ReviewExpert opinion on drug discovery
Deep learning tools to accelerate antibiotic discovery.
Review in Expert opinion on drug discovery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Marine Antimicrobial Peptides: From Ocean Biodiversity to Genome Mining, Multi-Omics Discovery, and Biotechnological Innovation in the Battle Against Antimicrobial Resistance.Probiotics and antimicrobial proteins · 2026Review
- Proteotoxic Stress Bioreporter Enables Mechanism-Informed Antibiotic Discovery.Journal of natural products · 2026Article
- Goat gut microbiome as a reservoir for microorganism-encoded short peptides: regulation by host development age and nematode challenge.NPJ biofilms and microbiomes · 2026Article
- Disease-Causing Mechanisms and Therapeutic Targets in Infectious Diseases: Implications for Clinical Management and Public Health.Biomedicines · 2026Review
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- Review
- A generative artificial intelligence approach for peptide antibiotic optimization.Nature machine intelligence · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Artificial intelligence-driven anticancer peptide discovery.iMetaOmics · 2025Review
- Article
- Structure-Antimicrobial Activity Relationships of Recombinant Host Defence Peptides Against Drug-Resistant Bacteria.Microbial biotechnology · 2025Article
- Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.Molecular diversity · 2025Review
- Novel Antibacterial Approaches and Therapeutic Strategies.Antibiotics (Basel, Switzerland) · 2025Review
- Reviving the past for a healthier future: ancient molecules and remedies as a solution to the antibiotic crisis.Future microbiology · 2025Review
- An explainable deep learning platform for molecular discovery.Nature protocols · 2025Review
- How AI can help us beat AMR.npj antimicrobials and resistance · 2025Review
- Advance in peptide-based drug development: delivery platforms, therapeutics and vaccines.Signal transduction and targeted therapy · 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
5 authors.
Funding
Abstract
introductionAs machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity. AREAS COVERED: This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics. EXPERT OPINION: Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.
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.