ReviewJournal of computer-aided molecular design2025
Artificial intelligence in protein-based detection and inhibition of AMR pathways.
Review in Journal of computer-aided molecular design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Quantitative structure-activity relationship characterization and modeling of length-varying bioactive peptides.Journal of computer-aided molecular design · 2026Article
- Genomic Insights into Chromosomal Colistin Resistance and Virulence-Resistance Convergence in MDR/XDRPathogens (Basel, Switzerland) · 2026Article
- From fragmented to integrated surveillance in LMICs: digital pathways for outbreak detection and vaccine intelligence.Frontiers in public health · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial Resistance (AMR) is a global concern demanding high-throughput and precise AMR surveillance strategies. This review provides a comprehensive list of Artificial Intelligence (AI) driven frameworks widely employed in the early detection, structural characterization, and designing of novel inhibitors to block the resistance pathways critical for AMR. Deep learning algorithms including DeepGO, DeepGOPlus, DeepGO-SE, PFresGO, DPFunc, ProtENN and graph-based architectures of GraphSite, GrASP enables precise functional annotation of resistance-associated proteins. AI-guided protein modeling performed by AlphaFold, RoseTTAFold, ProtGPT-2, ESMFold etc. generates high resolution 3D conformations, further utilized in performing molecular docking via tools like AutoDock, DeepDocking and DeepChem and analyzed with tools like DeepDriveMD, TorchMD, and PRITHVI, which can perform real-time molecular dynamics simulations. Identification of relevant resistant biomarkers from mass-spectrometry profiles can also be achieved with the help of DeepNovo, Casanovo, or Prosit. Tools like DeepARG, HMD-ARG, and BacEffluxPred enables identification of unannotated resistance genes from metagenomic samples. Natural Language Processing (NLP) and Large Language-based models (LLM) facilitate identification of resistant determinants via literature mining enabling regulatory network mapping and rational inhibitor design. Furthermore, AI-mediated de-novo inhibitor design is achieved using Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), diffusion and flow-matching based frameworks serve as potential options for enhancing diagnostic interventions against resistant phenotypes. AI-based protein-protein interaction predictors include DeepInteract, Pred_PPI, PLIP, DeepAIPs-Pred, DeepAIPs-SFLA, SBSM-Pro, Deep Stacked-AVPs, and pNPs-CapsNet help in understanding how resistance proteins interact with each other enabling precise identification of AMR-modulating peptides and supports the modeling of novel antibiotics for blocking interactions and disrupting resistance pathways.
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41286386What 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.