{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:42:26Z","timestamp":1784302946873,"version":"3.55.0"},"reference-count":54,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472192; 62372205"],"award-info":[{"award-number":["62472192; 62372205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["KJ02502022-0450"],"award-info":[{"award-number":["KJ02502022-0450"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Language Commission Key Research Project","award":["ZDI145-56"],"award-info":[{"award-number":["ZDI145-56"]}]},{"name":"Self-determined Research Funds of CCNU from the Colleges\u2019 Basic Research and Operation of MOE","award":["CCNU25JC008"],"award-info":[{"award-number":["CCNU25JC008"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    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.,\n                    <jats:italic>E. coli<\/jats:italic>\n                    or\n                    <jats:italic>S. aureus<\/jats:italic>\n                    ), 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.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1013833","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T18:49:51Z","timestamp":1767034191000},"page":"e1013833","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["A novel generative framework for designing pathogen-targeted antimicrobial peptides with 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Moformer: Multi-objective antimicrobial peptide generation based on conditional transformer joint multi-modal fusion descriptor. arXiv preprint 2024. arXiv:240602610","DOI":"10.21203\/rs.3.rs-4990322\/v1"},{"issue":"4","key":"pcbi.1013833.ref022","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1038\/s42256-021-00310-5","article-title":"Expanding functional protein sequence spaces using generative adversarial networks","volume":"3","author":"D Repecka","year":"2021","journal-title":"Nature Machine Intelligence."},{"issue":"5","key":"pcbi.1013833.ref023","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1021\/acs.jcim.0c01441","article-title":"AMPGAN v2: machine learning-guided design of antimicrobial peptides","volume":"61","author":"CM Van Oort","year":"2021","journal-title":"J Chem Inf Model."},{"issue":"1","key":"pcbi.1013833.ref024","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1038\/s41467-023-36994-z","article-title":"Discovering highly potent antimicrobial peptides with deep generative model HydrAMP","volume":"14","author":"P Szymczak","year":"2023","journal-title":"Nat Commun."},{"key":"pcbi.1013833.ref025","doi-asserted-by":"crossref","unstructured":"Hou K, Zhao W, He T. Physicochemical property-guided conditional VAE for antimicrobial peptides generation. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2024. p. 3279\u201382. https:\/\/doi.org\/10.1109\/bibm62325.2024.10822555","DOI":"10.1109\/BIBM62325.2024.10822555"},{"key":"pcbi.1013833.ref026","doi-asserted-by":"crossref","unstructured":"Zhao W, Hou K, Shen Y, Hu X. A conditional denoising VAE-based framework for antimicrobial peptides generation with preserving desirable properties. 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Deep learning regression model for antimicrobial peptide design. BioRxiv. 2019:692681.","DOI":"10.1101\/692681"},{"issue":"1","key":"pcbi.1013833.ref032","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/s44259-024-00051-6","article-title":"Antibiotic susceptibility testing using minimum inhibitory concentration (MIC) assays","volume":"2","author":"N Kade\u0159\u00e1bkov\u00e1","year":"2024","journal-title":"NPJ Antimicrob Resist."},{"issue":"801","key":"pcbi.1013833.ref033","article-title":"A host defense peptide-mimicking prodrug activated by drug-resistant Gram-negative bacterial infections","volume":"17","author":"J Xie","year":"2025","journal-title":"Sci Transl Med."},{"key":"pcbi.1013833.ref034","article-title":"Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation","volume":"44","author":"NA O\u2019Leary","year":"2016","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"pcbi.1013833.ref035","doi-asserted-by":"crossref","first-page":"6234","DOI":"10.1038\/s41467-023-41454-9","article-title":"A pharmacophore-guided deep learning approach for bioactive molecular generation","volume":"14","author":"H Zhu","year":"2023","journal-title":"Nat Commun."},{"key":"pcbi.1013833.ref036","doi-asserted-by":"crossref","unstructured":"Bowman SR, Vilnis L, Vinyals O, Dai A, Jozefowicz R, Bengio S. Generating sentences from a continuous space. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. 2016. https:\/\/doi.org\/10.18653\/v1\/k16-1002","DOI":"10.18653\/v1\/K16-1002"},{"key":"pcbi.1013833.ref037","unstructured":"He Z, Sun T, Wang K, Huang X, Qiu X. Diffusionbert: Improving generative masked language models with diffusion models. arXiv preprint 2022. https:\/\/arxiv.org\/abs\/2211.15029"},{"key":"pcbi.1013833.ref038","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"J Ho","year":"2020","journal-title":"Advances in Neural Information Processing Systems."},{"issue":"15","key":"pcbi.1013833.ref039","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"A Rives","year":"2021","journal-title":"Proc Natl Acad Sci U S A."},{"issue":"1","key":"pcbi.1013833.ref040","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf023","article-title":"Deep learning-based design and experimental validation of a medicine-like human antibody library","volume":"26","author":"N Rajagopal","year":"2024","journal-title":"Brief Bioinform."},{"issue":"6","key":"pcbi.1013833.ref041","doi-asserted-by":"crossref","first-page":"3287","DOI":"10.1109\/TIT.2020.2996543","article-title":"Levenshtein distance, sequence comparison and biological database search","volume":"67","author":"B Berger","year":"2021","journal-title":"IEEE Trans Inf Theory."},{"issue":"19","key":"pcbi.1013833.ref042","doi-asserted-by":"crossref","first-page":"10821","DOI":"10.3390\/ijms251910821","article-title":"Enhancing antimicrobial peptide activity through modifications of charge, hydrophobicity, and structure","volume":"25","author":"P Gagat","year":"2024","journal-title":"Int J Mol Sci."},{"issue":"23","key":"pcbi.1013833.ref043","doi-asserted-by":"crossref","first-page":"5862","DOI":"10.3390\/ijms20235862","article-title":"Insect cecropins, antimicrobial peptides with potential therapeutic applications","volume":"20","author":"D Brady","year":"2019","journal-title":"Int J Mol 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