ArticleBriefings in bioinformatics2026
ANIA: an inception-attention network for predicting minimum inhibitory concentration of antimicrobial peptides.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- Vertex Assignment for Frequency Chaos Game Representation of Proteins Affects Classification Performance.Computational and structural biotechnology journal · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Antimicrobial resistance poses a significant challenge to conventional antibiotics, underscoring the urgent need for alternative therapeutic strategies. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their broad-spectrum antibacterial activity and distinct mechanisms of action. This study presents ANIA, a deep learning framework developed to predict the minimum inhibitory concentration (MIC) values of AMPs against three clinically significant bacteria: Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. ANIA leverages Chaos Game Representation (CGR) to transform AMP sequences into frequency-based image features, which are subsequently processed through a hybrid architecture comprising stacked Inception modules, a Transformer encoder, and a regression head. This integrative architecture enables ANIA to capture both local motif-based features and global contextual patterns embedded within AMP sequences. In benchmarking experiments, ANIA achieved notably superior performance compared to existing tools, including ESKAPEE-Pred, AMPActiPred, and esAMPMIC, achieving higher correlation coefficients and lower predictive errors across all bacteria targets, with the most pronounced improvement observed for P. aeruginosa, a pathogen renowned for its multidrug resistance. Specifically, ANIA achieved PCCs of 0.75-0.79 and MSEs of 0.23-0.26 across all species. Furthermore, motif-based interpretability analyses combining Grad-CAM visualizations, correlation heatmaps, motif frequency distributions, and hydrophobicity profiling revealed biologically meaningful subregions within the CGR matrix that are plausibly associated with antimicrobial efficacy. In conclusion, this study develops ANIA as a robust predictive tool for MIC estimation, offering valuable insights into the design of effective antimicrobial agents and contributing to the fight against antimicrobial resistance. A user-friendly web server for ANIA is available at https://biomics.lab.nycu.edu.tw/ANIA/.
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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.