Fung-AI: An AI/ML-driven Pipeline for Antifungal Peptide Discovery
Daniel S. Berman, Libby M. Lewis, Tom D. Curtis, Olivia N. Tiburzi, Daniel F. Q. Smith, Arturo Casadevall, Laura J. Dunphy
Abstract
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, in silico antifungal and hemolytic classifiers were built to further prioritize AI-generated peptides for experimental validation.
Introduction
A major threat of growing global concern, fungal pathogens have been estimated to cause at least 2.5 million infection-related deaths per year, while additionally driving the spoilage of between 10–23% of annual pre-harvest crop yields. The negative impact of fungal pathogens has been exacerbated in recent years by both a general lack of known classes of antifungal drugs, as well as the emergence of multi- and pan-drug-resistant clinical isolates.
Materials and Methods:
The antifungal peptide dataset consisted of 9,388 peptides from published literature, DRAMP 4.0, and CAMPR4. Antifungal peptides from CAMPR4 and DRAMP 4.0 could be naturally occurring or synthetic, but had to have experimentally validated antifungal properties. Peptides only hypothesized to be antifungal were not included in the dataset. A total of 2,204 peptides were gathered from the CAMPR4 database, of which 982 (44.6%) were antifungal and 1,222 (55.4%) were non-antifungal. An additional 1,811 peptides, of which all were antifungal, were pulled from the DRAMP 4.0 database. Finally, 5,373 peptides from Sharma et al. were included, of which 1,932 (36.0%) were antifungal and 3,441(64.0%) were non-antifungal.
Discussion:
Here, we have developed and experimentally validated the Fung-AI pipeline, an AI/ML supported approach to design de novo antifungal peptides. We generated ~10,000 candidate peptides in silico with a custom GAN, computationally down-selected hits, and evaluated peptide antifungal activity across a panel of fungal pathogens of relevance in agriculture and human health. Testing fewer than 20 peptides across two phases of experimental validation, we identified one cluster of peptides from which five out of nine synthesized peptides (55%) displayed activity against at least one fungal species.
Citation: Berman DS, Lewis LM, Curtis TD, Tiburzi ON, Smith DFQ, Casadevall A, et al. (2026) Fung-AI: An AI/ML-driven pipeline for antifungal peptide discovery. PLoS Comput Biol 22(6): e1014105. https://doi.org/10.1371/journal.pcbi.1014105
Editor: Dirk Walther, Max Planck Institute of Molecular Plant Physiology: Max-Planck-Institut fur molekulare Pflanzenphysiologie, GERMANY
Received: March 6, 2026; Accepted: May 29, 2026; Published: June 15, 2026.
Copyright: © 2026 Berman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data and code used in model development and figure generation can be found at https://github.com/jhuapl-bio/fungai.
Funding: This research was supported by internal funding from the Johns Hopkins University Applied Physics Laboratory (JHU/APL). Authors DSB, LML, TDC, ONT, and LJD were employed at JHU/APL at the time of this work and their work on the study was performed within the scope of that employment. DFQS and AC were supported in part by the NIAID (U19-AI189183) which had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
