Harnessing Sequence Embedding and Ensemble Learning to Identify Antifungal Peptides with Low Hemolytic Risk.

The increasing prevalence of fungal infections represents a growing threat to human health, driven in part by the misuse of antibiotics and the rising incidence of resistance to conventional antifungal agents. Antifungal peptides (AFPs) have emerged as promising alternatives due to their diverse mechanisms of action and their relatively low propensity to develop resistance. To facilitate the systematic discovery of AFPs, we developed AI4AFP. This computational framework integrates curated antifungal peptide resources with advanced machine learning approaches to predict antifungal potential directly from peptide sequences. Using a comprehensive data set, we constructed a seven-model ensemble that combines multiple sequence encoding strategies, including ProtBERT-BFD, PC6, and Doc2Vec, with diverse learning algorithms, including random forests, support vector machines, convolutional neural networks, and fine-tuned BERT models. This ensemble demonstrated robust performance on an independent test set, achieving 0.94 in accuracy and 0.89 in Matthews correlation coefficient, outperforming existing AFP prediction methods. Importantly, the predicted AFP score is intended to reflect the general antifungal potential rather than species-specific potency. Experimental validation against representative fungal pathogens, including