{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:38:22Z","timestamp":1792175902485,"version":"4.4.0"},"reference-count":70,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"6","content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Adv."],"published-print":{"date-parts":[[2025,2,7]]},"abstract":"<jats:p>\n            Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AMPs with sufficient novelty and diversity, and they are rarely applied to the generation of antifungal peptides. Here, we develop an alternative pipeline grounded in a diffusion model and molecular dynamics for the de novo design of AMPs. The peptides generated by our pipeline have lower similarity and identity than those of other reported methodologies. Among the 40 peptides synthesized for an experimental validation, 25 exhibit either antibacterial or antifungal activity. AMP-29 shows selective antifungal activity against\n            <jats:italic>Candida glabrata<\/jats:italic>\n            and in vivo antifungal efficacy in a murine skin infection model. AMP-24 exhibits potent in vitro activity against Gram-negative bacteria and in vivo efficacy against both skin and lung\n            <jats:italic>Acinetobacter baumannii<\/jats:italic>\n            infection models. The proposed approach offers a pipeline for designing diverse AMPs to counteract the threat of antibiotic resistance.\n          <\/jats:p>","DOI":"10.1126\/sciadv.adp7171","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T18:59:36Z","timestamp":1738781976000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":88,"title":["Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides"],"prefix":"10.1126","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6400-9973","authenticated-orcid":true,"given":"Yeji","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9375-0757","authenticated-orcid":true,"given":"Minghui","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fujing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9065-2607","authenticated-orcid":true,"given":"Yuemei","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9524-2971","authenticated-orcid":true,"given":"Huaizu","family":"Luan","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojie","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenchang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, Guangdong, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6406-5095","authenticated-orcid":true,"given":"Wenjie","family":"Fang","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Molecular Medical Mycology, Shanghai Institute of Mycology, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0621-2779","authenticated-orcid":true,"given":"Gang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3300-1811","authenticated-orcid":true,"given":"Hongxiang","family":"Lou","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0187-9363","authenticated-orcid":true,"given":"Wenqiang","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2010.00134"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41579-021-00649-x"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baac011"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-022-01226-0"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/675391"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30327-3"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00073"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.copbio.2022.102718"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2019.03097"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.omtn.2020.05.006"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00841"},{"key":"e_1_3_2_13_2","first-page":"1","article-title":"AMPlify: Attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens","volume":"23","author":"Li C.","year":"2022","unstructured":"C. Li, D. Sutherland, S. A. Hammond, C. Yang, F. Taho, L. Bergman, S. Houston, R. L. Warren, T. Wong, L. M. N. Hoang, C. E. Cameron, C. C. Helbing, I. Birol, AMPlify: Attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC Genomics 23, 1\u201315 (2022).","journal-title":"BMC Genomics"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-022-00991-2"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-024-01201-x"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2024.05.013"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00175"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-36994-z"},{"key":"e_1_3_2_19_2","unstructured":"A. Ulhaq N. Akhtar G. Pogrebna Efficient diffusion models for vision: A survey. arXiv:2210.09292 [cs.CV] (2022)."},{"key":"e_1_3_2_20_2","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume":"11","author":"Dhariwal P.","year":"2021","unstructured":"P. Dhariwal, A. Nichol, Diffusion models beat GANs on image synthesis. Adv. Neural Inf. Process. Syst. 11, 8780\u20138794 (2021).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_2_21_2","unstructured":"N. Gruver S. Stanton N. Frey T. G. J. Rudner I. Hotzel J. Lafrance-Vanasse A. Rajpal K. Cho A. G. Wilson Protein design with guided discrete diffusion. arXiv:2305.20009 [cs.LG] (2023)."},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Sarah Alamdari Nitya Thakkar Rianne van den Berg Alex X. Lu Nicolo Fusi Ava P. Amini K. K. Yang Protein generation with evolutionary diffusion: sequence is all you need. bioRxiv 556673 [Preprint] (2023). https:\/\/doi.org\/10.1101\/2023.09.11.556673.","DOI":"10.1101\/2023.09.11.556673"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"S. Zhang Z. Jiang R. Huang S. Mo L. Zhu P. Li Z. Zhang E. Pan X. Chen Y. Long PRO-LDM: Protein sequence generation with a conditional latent diffusion model. bioRxiv 554145 [Preprint] (2023). https:\/\/doi.org\/10.1101\/2023.08.22.554145.","DOI":"10.1101\/2023.08.22.554145"},{"key":"e_1_3_2_24_2","unstructured":"J. Song C. Meng S. Ermon Denoising diffusion implicit models paper presented at the 9th International Conference on Learning Representations (ICLR 2021) Vienna Austria 4 May 2021."},{"key":"e_1_3_2_25_2","unstructured":"Z. Guo J. Liu Y. Wang M. Chen D. Wang D. Xu J. Cheng Diffusion models in bioinformatics: A new wave of deep learning revolution in action. arXiv:2302.10907 [cs.LG] (2023)."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"e_1_3_2_27_2","unstructured":"D. P. Kingma M. Welling Auto-encoding variational Bayes paper presented at the 2nd International Conference on Learning Representations (ICLR 2014 Conference Track) Banff Canada 14 to 16 April 2014."},{"key":"e_1_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Y. Wang M. Song F. Liu Z. Liang R. Hong Y. Dong H. Luan X. Fu W. Yuan W. Fang G. Li H. Lou W. Chang Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides Zenodo (2024); https:\/\/doi.org\/10.5281\/zenodo.13762213.","DOI":"10.1126\/sciadv.adp7171"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"R. Rombach A. Blattmann D. Lorenz P. Esser B. Ommer \u201cHigh-resolution image synthesis with latent diffusion models\u201d in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE 2022) pp. 10674\u201310685.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000089"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkaa1100"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1039\/D1SC01050F"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-021-00689-x"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1039\/D1SC01713F"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.peptides.2015.07.023"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1128\/spectrum.01240-22"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aan4044"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpharm.2017.04.082"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1128\/AAC.44.7.1803-1808.2000"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10096-014-2219-3"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-019-0058-8"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1128\/CMR.00181-19"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11033-021-06690-6"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1791-1"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03218-w"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkab1080"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkab651"},{"key":"e_1_3_2_48_2","first-page":"1","article-title":"Deep learning for novel antimicrobial peptide design","volume":"11","author":"Wang C.","year":"2021","unstructured":"C. Wang, S. Garlick, M. Zloh, Deep learning for novel antimicrobial peptide design. Biomolecules 11, 1\u201317 (2021).","journal-title":"Biomolecules"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz260"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-421"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl158"},{"key":"e_1_3_2_52_2","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"721","author":"Paszke A.","year":"2019","unstructured":"A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. K\u00f6pf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, PyTorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 721, 8026\u20138037 (2019).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_2_53_2","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"2020","author":"Ho J.","year":"2020","unstructured":"J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 2020, 6840\u20136851 (2020).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_2_54_2","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume":"139","author":"Nichol A.","year":"2021","unstructured":"A. Nichol, P. Dhariwal, Improved denoising diffusion probabilistic models. Proc. Mach. Learn. Res. 139, 8162\u20138171 (2021).","journal-title":"Proc. Mach. Learn. Res."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac094"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac343"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab160"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.8b00529"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1021\/ct300646g"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.5b00209"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1021\/jp071097f"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1063\/5.0018516"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.2408420"},{"key":"e_1_3_2_64_2","doi-asserted-by":"crossref","unstructured":"R. Gowers M. Linke J. Barnoud T. Reddy M. Melo S. Seyler J. Doma\u0144ski D. Dotson S. Buchoux I. Kenney O. Beckstein \u201cMDAnalysis: A python package for the rapid analysis of molecular dynamics simulations\u201d in Proceedings of the 15th Python in Science Conference (SciPy 2016) vol. 98 pp. 98\u2013105.","DOI":"10.25080\/Majora-629e541a-00e"},{"key":"e_1_3_2_65_2","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa F.","year":"2011","unstructured":"F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, \u00c9. Duchesnay, Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.20945"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.str.2021.08.003"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2015.06.001"},{"key":"e_1_3_2_70_2","unstructured":"Clinical and Laboratory Standards Institute (CLSI) Reference Method for Broth Dilution Antifungal Susceptibility Testing of Yeasts; Approved Standard CLSI document M27-A (CLSI ed. 33 2008)."},{"key":"e_1_3_2_71_2","unstructured":"Clinical and Laboratory Standards Institute (CLSI) M100: Performance Standards for Antimicrobial Susceptibility Testing (CLSI ed. 31 2021)."}],"container-title":["Science Advances"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/sciadv.adp7171","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T16:09:16Z","timestamp":1743782956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/sciadv.adp7171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,7]]},"references-count":70,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,2,7]]}},"alternative-id":["10.1126\/sciadv.adp7171"],"URL":"https:\/\/doi.org\/10.1126\/sciadv.adp7171","relation":{},"ISSN":["2375-2548"],"issn-type":[{"value":"2375-2548","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,7]]},"assertion":[{"value":"2024-04-09","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"eadp7171"}}