{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T19:36:48Z","timestamp":1791401808841,"version":"4.3.3"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014105","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000}}],"reference-count":58,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Johns Hopkins University Applied Physics Laboratory","award":["Internal research funding"],"award-info":[{"award-number":["Internal research funding"]}]},{"DOI":"10.13039\/100000060","name":"NIAID","doi-asserted-by":"crossref","award":["U19-AI189183"],"award-info":[{"award-number":["U19-AI189183"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Emerging fungal pathogens represent a concerning threat to both global health and food security. In this study, we aimed to address our rising vulnerability to fungal pathogens through the development of the Fung-AI pipeline: an AI\/ML-driven approach for antifungal discovery. A generative adversarial network (GAN) was trained to generate novel candidate antifungal peptide sequences. Next,\n                    <jats:italic>in silico<\/jats:italic>\n                    antifungal and hemolytic classifiers were built to further prioritize AI-generated peptides for experimental validation. From a pool of ~10,000 candidates, thirteen peptides were selected for testing over two-stages of experimentation. Five peptides were found to display mild antifungal activity against the wheat pathogen,\n                    <jats:italic>Fusarium graminearum<\/jats:italic>\n                    , with minimal inhibitory concentrations (MICs) ranging from 250 \u00b5g\/mL to 500 \u00b5g\/mL. Four of the five peptides also showed activity against the human pathogen,\n                    <jats:italic>Candida albicans<\/jats:italic>\n                    (MIC: 500 \u00b5g\/mL). Two of our AI-generated antifungal peptides additionally demonstrated low cytotoxicity in HepG2 human liver carcinoma cells (LC\n                    <jats:sub>50<\/jats:sub>\n                    \u2009&gt;\u2009704.2 \u00b5g\/mL) indicating that they may be useful as scaffolds for future optimization for therapeutic applications. None of our peptides were found to considerably inhibit the emerging pathogen\n                    <jats:italic>C. auris<\/jats:italic>\n                    , suggesting the need for pathogen-specific down-selection of candidate peptides. Overall, we present a proof-of-principle, generative-AI-based approach for the rapid design of\n                    <jats:italic>de novo<\/jats:italic>\n                    antifungal peptides.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014105","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:30:31Z","timestamp":1781544631000},"page":"e1014105","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fung-AI: An AI\/ML-driven pipeline for antifungal peptide discovery"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9176-7535","authenticated-orcid":true,"given":"Daniel S.","family":"Berman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6520-5988","authenticated-orcid":true,"given":"Libby M.","family":"Lewis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom D.","family":"Curtis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivia N.","family":"Tiburzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel F. 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Generative Adversarial Networks. arXiv. 2014. 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