{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:24:35Z","timestamp":1787851475233,"version":"build-2784847793"},"reference-count":57,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["23K24943"],"award-info":[{"award-number":["23K24943"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"award":["23K24943"],"award-info":[{"award-number":["23K24943"]}],"id":[{"id":"https:\/\/ror.org\/00hhkn466","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Identifying interleukin-6 (IL-6) and interleukin-13 (IL-13)-inducing peptides is important for drug discovery targeting cancer, immune disorders, and infectious diseases. However, experimental screening is costly and time-consuming. Machine learning and deep learning models have been developed that distinguish functional peptides from no-function ones, but their performance is limited by the small number of experimentally validated peptides. In this study, we propose a generative AI-driven data augmentation framework, GDA, and its prediction system, GDA-Pred, to improve the performance of state-of-the-art (SOTA) classifiers under limited data. GDA generates peptide sequences using three generative models: generative adversarial networks, diffusion models, and variational autoencoders. The framework is controlled by four hyperparameters: generative model type, sequence identity cutoff, probability threshold, and augmentation ratio. Because optimizing these hyperparameters is difficult with small datasets, we used anti-inflammatory peptide (AIP) data as a proof-of-concept to identify an effective reference hyperparameter setting. We evaluated GDA using stratified 5-fold cross-validation with cluster-based partitioning and a hold-out benchmark test. The GDA with the AIP-derived reference hyperparameter setting was then applied to SOTA classifiers to identify IL-6 and IL-13-inducing peptides as a case study. GDA-Pred consistently improved prediction performance for both cytokine-inducing peptide datasets, demonstrating the potential of generative AI to overcome data scarcity in peptide prediction.<\/jats:p>","DOI":"10.3390\/ijms27177688","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:47:13Z","timestamp":1787849233000},"page":"7688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GDA-Pred: Generative AI-Driven Data Augmentation for Improved Prediction of IL-6 and IL-13-Inducing Peptides"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4254-2214","authenticated-orcid":false,"given":"Hiroyuki","family":"Kurata","sequence":"first","affiliation":[{"name":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroto","family":"Tsuruta","sequence":"additional","affiliation":[{"name":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soyogu","family":"Shigetomi","sequence":"additional","affiliation":[{"name":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3324-793X","authenticated-orcid":false,"given":"Md.","family":"Harun-Or-Roshid","sequence":"additional","affiliation":[{"name":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-1322","authenticated-orcid":false,"given":"Kazuhiro","family":"Maeda","sequence":"additional","affiliation":[{"name":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.drudis.2014.10.003","article-title":"Peptide therapeutics: Current status and future directions","volume":"20","author":"Fosgerau","year":"2015","journal-title":"Drug Discov. 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