ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- Next-Generation Antimicrobial Peptides for Biofilm-Associated Infections: Engineering, Biomaterial Delivery and AI-Assisted Discovery.Antibiotics (Basel, Switzerland) · 2026Review
- Antimicrobial Peptides Based on the TisB Toxin: Toward Enhanced Activity and Synergy with Antibiotics.ACS omega · 2026Article
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- From Host-Derived Pressures to the Environmental Anti-Antimicrobial Peptides Resistome: Mechanisms, Reservoirs and Implications for Therapeutic Peptide Design.Marine drugs · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Unlocking New Bioactive Peptides from Coffee Pulp: A Metagenomics and AI-Driven Discovery Paradigm.Foods (Basel, Switzerland) · 2025Article
- A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Antimicrobial peptides (AMPs) play a crucial role in developing novel anti-infective drugs due to their broad-spectrum antimicrobial activity and lower likelihood of causing bacterial resistance. However, laboratory synthesis of AMPs is tedious and time-consuming. Existing computational methods have limited capability in optimizing multiple desired properties simultaneously. Here, a Multi-Property Optimizing Generative Adversarial Network (MPOGAN) is proposed to iteratively learn the relationship between peptides and multi-properties with a dynamically updated dataset. With the increase of the dataset quality, the ability of the model to design AMPs with multiple desired properties is improved. Through extensive computational tests, MPOGAN exhibits superior performance in generating AMPs with multiple desired properties, including potent antimicrobial activity, reduced cytotoxicity, and increased diversity. Ten designed AMPs are chemically synthesized, nine of which exhibited antimicrobial activity and low cytotoxicity. Notably, two of these peptides showed potent broad-spectrum antimicrobial activity coupled with reduced cytotoxicity, highlighting their potential for downstream applications.
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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.