ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Identification and Characterization of an In Silico Designed Membrane-Active Peptide with Antiviral Properties.
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 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
- Mechanism-Informed Machine Learning Enables Discovery of Oncolytic Peptides for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
Broad-spectrum antivirals are urgently needed to counter emerging viral threats. Targeting the viral envelope, an essential, conserved, and host-derived structure, offers a promising strategy with a low risk of resistance. Here, we report the in silico design and experimental characterization of P1.6, a 24-mer peptide generated using an evolutionary molecular dynamics (Evo-MD) platform and optimized to sense and exploit lipid packing defects in viral membranes. Among nine Evo-MD-derived candidates, P1.6 showed the strongest membrane-disruptive activity and inhibited HIV-1, Zika virus, and herpes simplex viruses with IC
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Registered trials
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