ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Nano-antimicrobial peptides (Nano-AMPs) to combat resistant gram-negative bacteria.Drug delivery and translational research · 2026Review
- AI-Based D-Amino Acid Substitution for Optimizing Antimicrobial Peptides to Treat Multidrug-Resistant Bacterial Infection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine learning-driven discovery of antimicrobial peptides againstFrontiers in pharmacology · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.Accounts of chemical research · 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
24 authors.
Funding
Abstract
Artificial intelligence (AI)-driven discovery of antimicrobial peptides (AMPs) is yet to fully utilize their three-dimensional (3D) structural characteristics, microbial species-specific antimicrobial activities, and mechanisms. Here, we constructed a QLAPD database comprising the sequence, structures, and antimicrobial properties of 12 914 AMPs. QLAPD underlies a multimodal, multitask, multilabel, and conditionally controlled AMP discovery (M3-CAD) pipeline, proposed for the de novo design of multi-mechanism AMPs to combat multidrug-resistant organisms (MDROs). This pipeline integrates generation, regression, and classification modules, using an innovative 3D voxel coloring method to capture the nuanced physicochemical context of amino acids, thus enhancing structural characterizations. QLX-3DV-1 and QLX-3DV-2, identified through M3-CAD, were found to demonstrate multiple antimicrobial mechanisms, notable activity against MDROs, and low toxicity. In vivo experiments were used to validate their antimicrobial effects with limited local and systemic toxicity. Overall, integrating 3D features, species-specific antimicrobial activities, and mechanisms enhanced AI-driven AMP discovery, making the M3-CAD pipeline a viable tool for de novo AMP design.
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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.