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A novel generative framework for designing pathogen-targeted antimicrobial peptides with programmable physicochemical properties
Journal article   Open access   Peer reviewed

A novel generative framework for designing pathogen-targeted antimicrobial peptides with programmable physicochemical properties

Weizhong Zhao, Kaijieyi Hou, Chang Tang, Yiting Shen, Jinlin Liu and Xiaohua Hu
PLoS computational biology, v 21(12), e1013833
29 Dec 2025
DOI: https://doi.org/10.1371/journal.pcbi.1013833
PMID: 41460918
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1371/journal.pcbi.1013833View
Published, Version of Record (VoR) Open

Abstract

Anti-Bacterial Agents - chemistry Anti-Bacterial Agents - pharmacology Antimicrobial Peptides - chemistry Antimicrobial Peptides - pharmacology Computer Simulation Escherichia coli - drug effects Humans Microbial Sensitivity Tests Staphylococcus aureus - drug effects Computational Biology Drug Design
Antimicrobial peptides (AMPs) are crucial in addressing the global crisis of bacterial resistance. However, there are still significant limitations in existing methods on de novo AMPs design, especially in designing AMPs with desirable physicochemical properties for specific bacterial pathogens. In this study, we propose a novel generative framework for designing pathogen-targeted antimicrobial peptides with programmable physicochemical properties. More specifically, a conditional Variational Autoencoder is first pretrained for generating AMPs with editable physicochemical properties. We then develop a conditional diffusion model to learn hidden representations of AMPs for targeting pathogens of interest, and construct corresponding MIC predictors for specific bacterial strains. Through comprehensive simulation experiments, we demonstrate that the proposed framework outperforms most existing models in terms of antimicrobial efficacy against specific bacterial targets. Moreover, through systematic screening and analysis, we have identified two star AMPs for each of the two target bacterial species (i.e., E. coli or S. aureus), both of which exhibit excellent performance in antibacterial activity, hemolytic properties, toxicity profiles, etc. Overall, this study provides the key technological support for developing next-generation intelligent platforms for antimicrobial agents design.

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Biochemical Research Methods
Mathematical & Computational Biology
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