ArticleNature communications2026
Discovery of potent low-toxicity antimicrobial peptides through diffusion modeling.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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4 authors.
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Abstract
The rapid emergence of multidrug-resistant bacteria has created an urgent need for improved antimicrobial discovery and screening platforms. Here, we present ARCADIAMP, a generative and virtual screening platform that couples an iterative-learning discrete denoising diffusion probabilistic model with a two-stage Evolutionary Scale Modeling 2 (ESM2)-based antibacterial activity classifier to generate, classify, and prioritize potent AMPs with high activity, low toxicity, and favorable serum stability. Eight of the ten experimentally screened peptide candidates showed antimicrobial activity (MIC ≤ 32 μg/mL), while one generated candidate, Arcinin, demonstrated strong activity against ESKAPE pathogens (MIC 8-32 μg/mL), low hemolytic activity (LC
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