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Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to developing drug resistance. An AFP <jats:italic>de novo<\/jats:italic> design method, Deep Learning\u2010Quantitative Structure\u2012Activity Relationship Empirical Screening (DL\u2010QSARES), is developed by integrating deep learning and quantitative structure\u2012activity relationship \u00a0empirical screening. After generating candidate AFPs (c_AFPs) through the recombination of dominant amino acids and dipeptide compositions, natural language processing models are utilized and quantitative structure\u2012activity relationship (QSAR) approaches based on physicochemical properties to screen for promising c_AFPs. Forty\u2010nine promising c_AFPs are screened, and their minimum inhibitory concentrations (MICs) against <jats:italic>C. albicans<\/jats:italic> are determined to be 3.9\u2013125 \u00b5g\u00a0mL<jats:sup>\u22121<\/jats:sup>, of which four leading c_AFPs (AFP\u20108, \u221210, \u221211, and \u221213) has MICs of &lt;10 \u00b5g\u00a0mL<jats:sup>\u22121<\/jats:sup> against the four tested pathogenic fungi, and AFP\u201013 has excellent therapeutic efficacy in the animal model.<\/jats:p>","DOI":"10.1002\/advs.202412488","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T05:22:02Z","timestamp":1739251322000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Deep Learning Combined with Quantitative Structure\u2012Activity Relationship Accelerates <i>De Novo<\/i> Design of Antifungal Peptides"],"prefix":"10.1002","volume":"12","author":[{"given":"Kedong","family":"Yin","sequence":"first","affiliation":[{"name":"Zhengzhou Key Laboratory of Functional Molecules for Biomedical Research Henan University of Technology  Zhengzhou Henan 450001 P. 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